Closed-loop rehabilitation of upper-limb dyskinesia after stroke: from natural motion to neuronal microfluidics

Abstract

This review proposes an innovative closed-loop rehabilitation strategy that integrates multiple subdomains of stroke science to address the global challenge of upper-limb dyskinesia post-stroke. Despite advancements in neural remodeling and rehabilitation research, the compartmentalization of subdomains has limited the effectiveness of current rehabilitation strategies. Our approach unites key areas—including the post-stroke brain, upper-limb rehabilitation robotics, motion sensing, metrics, neural microfluidics, and neuroelectronics—into a cohesive framework designed to enhance upper-limb motion rehabilitation outcomes. By leveraging cutting-edge technologies such as lightweight rehabilitation robotics, advanced motion sensing, and neural microfluidic models, this strategy enables real-time monitoring, adaptive interventions, and personalized rehabilitation plans. Furthermore, we explore the potential of closed-loop systems to drive neural plasticity and functional recovery, offering a transformative perspective on stroke rehabilitation. Finally, we discuss future directions, emphasizing the integration of emerging technologies and interdisciplinary collaboration to advance the field. This review highlights the promise of closed-loop strategies in achieving unprecedented integration of subdomains and improving post-stroke upper-limb rehabilitation outcomes.

Introduction

Strokes are devastating events that often result in death or permanent disability [1, 2], with post-stroke sequelae severely impairing individuals’ ability to engage with and explore the world. Statistics indicate that approximately 80% of stroke patients experience upper-limb dyskinesia as a sequela, and around 50% of these individuals continue to struggle with mobility issues four years after the event [3]. It is encouraging to note that significant progress has been made in the areas of stroke prevention [1, 4], pathogenesis [4, 5], treatment [1, 4, 6], and rehabilitation [6, 7]. Nevertheless, progress has been slow because many subdomains within stroke science are fundamentally siloed from each other. To explain this, stroke research is conducted in a variety of perspectives ranging from cells [7, 8] to animal [9] and human [6]; research disciplines involve medicine, biology, engineering, and multidisciplinary intersections; and research scales range from the micron (neurone) [8] to the millimeter (sensor) [10], and the metre (rehabilitative robotic) [10, 11]. Though extensive research has been conducted, views or methods integrating across these subdomains are lacking.

Integrating subdomains is critical for advancing stroke rehabilitation, as it enables the translation of insights from one field into actionable solutions in another. For example, breakthroughs in nerve regeneration [7, 12] and brain plasticity [13] have informed the development of robotic assistance systems that facilitate neural remodeling [14, 15]. Similarly, the convergence of advancements in motion sensing and neuroelectronics is anticipated to yield closed-loop systems that adjust rehabilitation protocols in real time based on patient performance. The combination of brain-computer interfaces with functional electrical stimulation has enhanced motor recovery by establishing a direct link between neural activity and muscle activation. Similarly, integrating electrophysiology with stroke pathogenesis research has revealed that abnormalities in atrial electrical activity are closely associated with thrombogenic mechanisms. This discovery has driven the development and widespread clinical adoption of anticoagulant therapies, including warfarin and novel oral anticoagulants, for stroke prevention in patients with atrial fibrillation. Furthermore, the integration of neuroimaging and pathophysiology has clarified the time window and salvage potential of the ischemic penumbra, leading to the establishment of intravenous thrombolytic agents, such as recombinant tissue-type plasminogen activator, as the standard treatment for acute ischemic stroke. Additionally, the convergence of molecular biology and imaging techniques has elucidated the mechanisms underlying post-stroke blood-brain barrier disruption, including the activation of matrix metalloproteinases (MMPs). These insights have spurred the development and testing of MMP inhibitors aimed at mitigating post-stroke brain edema. Collectively, these examples highlight the transformative impact of interdisciplinary collaboration in advancing stroke rehabilitation and therapeutic strategies.

The integration of subdomains in the rehabilitation of post-stroke upper-limb dyskinesia holds significant potential to overcome the limitations of traditional rehabilitation approaches and enable more comprehensive, precise, and personalized treatment through interdisciplinary collaboration. Upper-limb dysfunction involves multi-level impairments, including disruption of neural networks in the brain, dysregulation of muscle activity, and loss of motor control [4,5,6,7]. Single-domain interventions often fail to address these complex issues holistically. Recent advances in neuroscience have highlighted the central role of neuroplasticity in functional recovery [1, 16,17,18]. Concurrently, rehabilitation robotics has emerged as a powerful tool to facilitate motor learning through task-oriented training, while motion sensing technology provides real-time feedback to quantify rehabilitation progress and optimize training regimens [10, 11]. Additionally, neuro-microfluidic platforms have been developed to simulate post-stroke neurological injury environments, enabling the validation and optimization of interventions such as electrical stimulation. By integrating these subdomains, a closed-loop rehabilitation system can achieve end-to-end regulation, from neural activity decoding to motor execution, while dynamically adapting rehabilitation strategies to meet individualized patient needs. This cross-domain synergy not only enhances rehabilitation efficiency but also offers novel insights into the mechanisms of post-stroke neural remodeling, ultimately advancing the field from experience-driven practices to data-driven rehabilitation science.

In this review, we highlight the closed-loop relationship between the subdomains, which forms a comprehensive medical and rehabilitation system for post-stroke upper-limb dyskinesia and integrates it into an interconnected entirety (as shown in Fig. 1). We discuss four main techniques and their interrelationships for upper-limb rehabilitation after stroke: (1) robot-assisted upper-limb motion rehabilitation, which is the research field of compensating for natural motion; (2) upper-limb motion sensing and metrics, which is the research field of converting natural motion into electrical signals; (3) upper-limb motion rehabilitation with electrical stimulation, which is the research field of electrical signals directly acting on the nervous system; and (4) microfluidics and microcircuits function as the final validation nodes and key feedback units, thereby providing substantial evidence for the remodeling of damaged neurons. This was preceded by a discussion of the basics related to stroke and neurocytes, which allows for a better understanding of closed-loop rehabilitation after stroke.

Fig. 1

Closed-loop post-stroke upper-limb motion rehabilitation strategies and corresponding breakthrough technologies. In the figure, arrows indicate the information flow or interrelationships, and coloured blocks indicate techniques for forming key components of embodiments. Upper-limb motion rehabilitation (robot-assisted) could serve as a mainstream treatment for post-stroke sequelae. Sensing and metrics technologies advance the flourishing of personalised medicine and participatory sports medicine and enable the restoration of the electrical signals from natural human motion through decoding and informatics transformations. Microfluidics has facilitated a profound understanding and exploration of cellular behaviour and function; especially as microfluidic circuits can connect restored electrical signals to nerve cells

The article begins with a fundamental understanding of post-stroke brain changes (Sect. 1) and progresses to the development and integration of advanced techniques for upper-limb rehabilitation. Section 2 introduces upper-limb rehabilitation robots, highlighting their role in facilitating motor recovery through task-specific training and adaptive assistance. Building on this, Sect. 3 delves into upper-limb motion sensing and metrics, which provide the critical data needed to decode natural motion, assess rehabilitation progress, and inform robotic interventions. Section 4 explores neuronal microfluidics and electronics, bridging the gap between neural modeling and clinical applications by demonstrating how these technologies can simulate stroke-induced damage and optimize rehabilitation strategies. Section 5 provides a comprehensive overview of the available perspectives and potential future directions, synthesising the insights from the preceding sections to propose innovative closed-loop systems. Finally, Sect. 6 concludes with a discussion of the key findings and their implications, integrating the interdisciplinary contributions of robotics, motion sensing, neural modelling, and electronics to advance stroke rehabilitation.

Post-stroke brain

Stroke is a disease caused by interruption of blood supply to the brain due to a sudden rupture or blockage (systemic hypotension in the absence of obstruction can also lead to stroke, which is a lesser effect in comparison) of a blood vessel. This interruption leads to a lack of oxygen and nutrients in the brain cells, which results in cell death and impairment of brain function. Ischaemic stroke is differentiated into a core zone (irreversibly damaged infarct foci, located in the central area) and a penumbra zone (an area of less reduced blood flow, surrounding the infarct foci and accounting for half of the total lesion volume) [16].

The primary goal of stroke treatment is the rapid restore of cerebral blood flow (neurons rely almost exclusively on oxidative phosphorylation to meet their high energy requirements), which is also a prerequisite for subsequent neuroprotection and rehabilitation [1] (otherwise secondary neuronal loss will result). The recovery process after a stroke is a complex and lengthy one in which nerve cell plasticity plays a crucial role [6]. The pathogenesis and resuscitation of stroke (see reviews by Diji. K [17]. and Seyed. E. K [18]. et al. for details) are beyond the scope of this review, and this section will discuss key results related to closed-loop mechanisms.

Brain imaging modalities

There is importance in understanding advanced methods of observing the brain to develop our cognition of the brain after a stroke. As Joana Cabral et al. [19]. have contributed to the observation of rat brain activity, neuroimaging is the driving force that inspires neuroscience. Neuroimaging can provide insight into the more subtle structures, functions, and interrelationships of the brain, especially in the moments following a stroke, and can provide information to determine possible treatment options. At present, the predominant neuroimaging methodologies are based on magnetic resonance. This technique exploits the spin behavior of atomic nuclei under a static magnetic field to generate high-resolution images of internal structures. The process involves excitation and rebound signal generation, spatial gradient encoding, signal detection, and image reconstruction. Magnetic Resonance Imaging (MRI) and Functional Magnetic Resonance Imaging (fMRI), both of which read the same data and produce high-resolution structural images, but the latter is more competitive for functional brain imaging and research [15, 20]. The fMRI measures changes in blood oxygen level-dependent signals, which reflect neural activity. When neurons become more active, their oxygen consumption increases, leading to a subsequent rise in regional cerebral blood flow. This hemodynamic response allows fMRI to identify brain regions engaged during specific tasks [15]. Additionally, fMRI offers relatively high temporal resolution compared to MRI, enabling the detection of dynamic changes in brain activity within a time scale of seconds.

Besides, MRI-derived technologies: Resting-State Functional Magnetic Resonance Imaging (rs-fMRI), Diffusion-Weighted Imaging (DWI), and Diffusion Tensor Imaging (DTI) are also fruitful research tools. The rs-fMRI can examine functional connectivity between brain regions in the resting state, giving it a broader capability to assess intrinsic changes in neuronal activity. Although rs-fMRI is more promising in the study of chronic degenerative neurological diseases [21], Yating Lv et al. demonstrated the ability of rs-fMRI in localising and assessing hypoperfused areas in acute stroke [22]. The principle of DWI is based on the anisotropic diffusion of water: the lesion prevents the random thermal movement of water molecules across the cell membrane [23], which can provide accurate volumetric information on the extent and geometry of the lesion [23] or even localise microglia and astrocytes [24]. DTI enables visualisation of the diffusion tensor (diffusion anisotropy and predominant direction of diffusion) of water molecules in tissues [25], which can distinguish fine white matter pathways to identify nerve fibre trajectories and neural connections. Lan-Wan. W’s latest study validates the ability of DTI to assess structural and functional changes in patients with chronic neurotrauma [26].

So, for the time being, MRI and its derivative techniques (MRI-dts) remain the most advanced, non-radioactive, and non-invasive techniques for observing the brain [27]. However, MRI-dts have significant limitations in terms of timely diagnosis, as the machinery is in a fixed radiology suite. Thus, portability is a future research priority: Matthew M. Y et al. achieved portable acquisition of stroke imaging at a low magnetic field strength of 0.064 T [28].

Characteristic responses of neuron after stroke

Post-stroke nerve cells usually undergo a series of reactions: oxidative stress (excessive accumulation of intracellular oxidants, such as free radicals), inflammatory response, excitotoxicity (increased intracellular calcium ions), neurotransmitter disorders, and finally cellular damage (apoptosis, necrosis, autophagy, etc.). These reactions are accompanied by changes in the characteristics of the nerve cells: morphology, products, and extracellular substances (as shown in Table 1). Understanding these changes can not only specifically suggest ways to mitigate cellular damage and promote repair, but, importantly, can assess the extent of cellular damage or the degree of repair, which is one of the key metrics of neuronal microfluidics. So, in this section, we focus on reviewing the characteristic changes in nerve cells after stroke.

The morphology of nerve cells changes significantly after a stroke due to the lack of oxygen and nutrients. In detail, when a cell needs energy adenosine triphosphate (ATP) drives various biological processes within the cell by releasing its phosphate groups and converting the stored chemical energy into the energy needed by the cell [29]. Oxidative phosphorylation in mitochondria under aerobic conditions is the main pathway for ATP production, whereas under anaerobic conditions, adenosine triphosphate is produced mainly by glycolysis. However, it has been shown that a glucose molecule produces about 38 ATP molecules through oxidative phosphorylation, while only about two ATP molecules are produced through glycolysis [30]. The disappointing productivity of glycolysis leads to a severe shortage of ATP supply and thus concentration in nerve cells. This “horrific” decline disrupts the normal intracellular ionic balance (e.g. potassium ion (K⁺) influx) and promotes the release of excitotoxic neurotransmitters (mainly glutamate) and inhibits reuptake. Glutamate binds to ionotropic N-Methyl-D-aspartate (NMDA) and α-amino − 3hydroxy – 5 – methyl – 4 - isoxazolepropionic acid (AMPA) receptors (iGluRs) and promotes substantial calcium ion (Ca²⁺) efflux [31]. Also, glutamate receptors promote the inward flow of excess sodium ion (Na⁺) and water [32]. This ultimately leads to increased neuronal cell volume and membrane deformation. In addition, ion homeostasis disorders accompanied by oxidative stress cause axonal degeneration and dendritic atrophy.

Table 1 Characteristic changes in nerve cells after stroke

Apoptosis is one of the typical phenomena of neuronal cell injury after stroke [33], which was first proposed in 1972 by Kerr JFR et al. [34] and quickly led to extensive discussion. Apoptosis is the result of cell signalling cascades, and reviews have shown that Ca²⁺ plays a key role in these responses [35]. Intra-neuronal Ca²⁺ overload leads to a series of cascading reactions: from excitotoxicity to activation of calpain [36], to effects on endoplasmic reticulum-mitochondrial Ca²⁺ transport [35], to the release of a variety of pro-apoptotic factors (mainly cytochrome C (Cytc) and apoptosis-inducing factors (AIFs)), and finally to the onset of cellular apoptosis. In this, the Cytc complex forms apoptosomes with apoptotic protein-activating factor-1 and Proaspase-9, thereby activating effector caspases [37, 38]. In addition to this, reactive oxygen species (ROS [39], which disrupts the plasma membrane, damages DNA [40], and inhibits mitochondrial function [39]), DNA damage (single/double strand breaks and formation of apurinic/apyrimidinic sites, etc.) [40], and exogenous factors (beyond the scope of this review) all contribute to apoptosis. Therefore, the main morphological changes of apoptosis in neuronal cells are cellular crumpling, cytoplasmic condensation, rupture of the nuclear membrane, and formation of apoptotic vesicles, all of which can be observed under the microscope.

Cellular autophagy is the process of breaking down and recycling organelles and cytoplasmic macromolecules, and usually plays a role in stress, starvation, and infection. As early as 2001, researchers had already found that apoptosis occurs in PC12 cells after serum deprivation and is accompanied by a dramatic decrease in cathepsin B expression and an increase in cathepsin D expression [41]. Subsequent studies in mice have shown that ischaemia-hypoxia is a direct contributor to autophagy in neuronal cells [42,43,44]. Ischaemia-hypoxia-induced autophagy in neuronal cells is usually accompanied by changes in cellular products: mTORC1 activity is decreased [45]; PI3K protein expression and Akt phosphorylation levels are significantly reduced [46]; and Hypoxia-inducible factor 1 (HIF-1) is activated and induces the transcription of genes, such as vascular endothelial growth factor and erythropoietin [47, 48]. Morphological changes in autophagy in neuronal cells are mainly the formation of autophagic vesicles, formation of vesicles in the cytoplasm, lysis of organelles, fragmentation of endoplasmic reticulum membranes, and condensation of chromatin in the nucleus.

Ferroptosis, a newly identified form of non-apoptotic cell death (iron-dependent and mediated) in 2012 by Dixon SJ et al., causes characteristic shrinkage of neuronal cell mitochondria [49]. Rapid ATP depletion and disruption of ionic homeostasis led to depolarisation of the nerve cell membrane and excessive glutamate release [30,31,32,33]. This process induces a cascade of reactions: phospholipase activation, phospholipid hydrolysis, arachidonic acid (AA) release [50], decreased glutathione peroxidase 4 Gpx4 expression [51], and loss of lipid peroxidation repair [51, 52], and ultimately the accumulation of redox-active iron in the nerve cell [51, 53]. In addition, nerve cells undergo environmental acidification after hypoxia, which promotes the release of iron from transferrin. The free iron is then readily taken up by the neurons, leading to intracellular iron accumulation [54]. And a review by Silvia. G et al. showed that iron ion dysregulation also leads to Ca²⁺ dysregulation [54]. Ferroptosis also leads to morphological changes such as chromatin condensation, cytoplasmic and organelle swelling, and plasma membrane rupture in neuronal cells [49, 51, 54].

In addition to the above, there are other forms of neuronal cell death (e.g., parthanatos, pyroptosis, and necrosis). It was shown that nicotinamide adenine dinucleotide (NAD+) depletion was rapid and PARP1 expression was increased in parthanatos [55]. Meanwhile, excitotoxicity caused nerve cells to produce peroxynitrite further promoting PARP1 activation [56]. The morphological changes of parthanatos are comparable to cell death, such as chromatinolysis and plasma membrane rupture. Cellular pyroptosis and necrosis are generally caused by factors external to the nerve cell (e.g., activation of microglia and release of necrotic and proinflammatory factors), so they are outside the scope of the nerve cell itself.

Neuroplasticity

Nerve cells are damaged or die as described above after stroke and retain plasticity after blood flow is restored. Neuroplasticity refers to the inherent capacity of the nervous system to undergo structural and functional adaptations in response to environmental changes, skill acquisition, and pathological conditions. This fundamental property enables the brain to accommodate novel demands and facilitate recovery from injury through the reorganization of neural networks, formation of new synaptic connections, and modulation of synaptic efficacy. As the neurobiological foundation of learning and memory processes, neuroplasticity represents a crucial mechanism underlying post-stroke functional recovery. Although not the primary focus of this review, neuroplasticity has emerged as a prominent area of investigation in recent years, with substantial empirical evidence demonstrating the remarkable regenerative potential of damaged neuronal circuits. The principal findings in this field are therefore briefly summarized to provide essential context for the current discussion. In 2023 Kenneth B. B. et al. conducted a phase I trial in 12 patients with upper-limb dyskinesia after stroke and demonstrated that deep brain stimulation to the cerebellar dentate nucleus promotes neuroplasticity [6]. In 2022 a review by Caroline. (A) et al. systematically discussed the mechanisms of axonal sprouting for post-stroke nerve repair, providing additional insights into neuroplasticity [57]. In 2021 Sampaio-Baptista. C. et al. used fMRI to conduct brief neurofeedback training in healthy individuals, demonstrating the potential for artificial changes to alter patterns of brain activity and stimulate bidirectional white matter plasticity [15]. In 2018 a review by Roger A. (B) et al. discusses two new approaches, stem cell-derived cell products and direct cell reprogramming, which provide new perspectives on neural repair [7]; in the same year, Teppei. E. et al. performed two-photon calcium imaging of layer 2/3 neurons in the motor cortex of marmosets (head-fixed and trained to perform an upper-limb motion task) and detected multiple neurons with task-related activity, which may serve as direct evidence of neuroplasticity [58].

Despite these advancements, significant gaps remain in translating experimental findings on neuroplasticity into effective clinical interventions. Many neuroplasticity studies rely on animal models or healthy human participants, which may not fully replicate the complex pathophysiology of stroke patients. For instance, while marmoset studies provide valuable insights into task-related neural activity [58], their relevance to human stroke recovery requires further validation. And stroke patients exhibit wide variability in lesion location, severity, and baseline neural function, making it difficult to generalize findings from small-scale trials (e.g., the 12-patient study by Kenneth B. B. et al. [6]) to broader populations. Furthermore, techniques like deep brain stimulation and two-photon imaging, while powerful, are often invasive or resource-intensive, limiting their scalability in clinical settings. Finally, many studies focus on short-term changes in neural activity or behavior, but the long-term sustainability of neuroplasticity-driven improvements remains poorly understood. In summary, while significant progress has been made in understanding neuroplasticity, translating these findings into clinical practice remains a formidable challenge. Bridging this gap will require innovative research strategies and a deeper understanding of the interplay between neural mechanisms and rehabilitation outcomes.

The above evidences underscore the brain’s remarkable ability to reorganize and adapt following stroke, providing a biological foundation for rehabilitation strategies aimed at restoring motor function. However, harnessing this potential requires targeted interventions that not only promote neural remodeling but also facilitate the practical recovery of upper-limb mobility. This is where upper-limb rehabilitation robots (ULrr) come into play. By integrating principles of neuroplasticity with advanced robotic technologies, ULrr systems offer a powerful tool to enhance motor recovery. These devices are designed to provide repetitive, task-specific training that aligns with the brain’s adaptive mechanisms, thereby maximizing the potential for functional improvement. In the following section, we explore the development, capabilities, and clinical applications of ULrr, highlighting their role in translating the principles of neuroplasticity into effective rehabilitation practices.

Upper-limb rehabilitation robot (ULrr)

The sequelae continue until the nerve cells recover, especially upper-limb dyskinesia. Several trials, studies, meta-analyses, and reviews in recent years have highlighted the important role of ULrrs in recovering motion and promoting neuroplasticity after stroke. ULrr represents a sophisticated mechanical apparatus designed to facilitate the restoration of motor functions in patients with upper-limb impairments [3, 59,60,61]. This advanced therapeutic device integrates multiple technological components, including bionic mechanical structures (or human-machine integration systems), drive system, multimodal sensors, centralized control units, and intuitive human-machine interaction interfaces. Through this comprehensive technological framework, ULrr has been extensively implemented across diverse rehabilitation settings, enabling the delivery of personalized therapeutic interventions and promoting the enhancement of upper extremity functional capabilities [62,63,64,65,66,67,68].

ULrrs are categorized based on their mechanical structure and interaction into end-driven and exoskeletal types. End-driven ULrrs connect to the distal upper limb via an end-effector to drive motion, while exoskeletal ULrrs feature multiple degrees of freedom (DOFs) and form a closed kinematic chain with the human upper limb to actuate joints. Studies indicate that exoskeletal ULrrs are more effective than end-driven types for upper-limb rehabilitation [60, 61, 66,67,68], as they better replicate natural motion and distribute forces across limb segments. As described below:

• End-driven ULrrs: Though cost-effective and simple, end-driven models often fail to replicate natural upper-limb motion and may cause joint irritation [83]. Their clinical use is limited to basic training tasks.

• Exoskeletal ULrrs: Dominating current research, exoskeletons restore natural motion and adapt to physiological characteristics, making them ideal for comprehensive rehabilitation. They have been shown to reduce interhemispheric inhibition and improve motor function [64, 65]. Furthermore, matching actuators to upper-limb joints and adapting exoskeletons to physiological characteristics are fundamental concepts in all ULrr designs.

Depending on the driving form, ULrr can also be subdivided into motor-driven, and pneumatic-driven, elastic-driven. The driver primarily drives the upper-limb joints: sternoclavicular (SC), acromioclavicular (AC), glenohumeral (GH), elbow (EL), wrist (WR), and finger (FIs) joints. Each type has distinct advantages and limitations in clinical applications:

• Motor-driven ULrrs: These robots provide precise control over limb positioning and are widely used in clinical settings. However, their high rigidity can be less friendly to soft tissues and may cause discomfort during prolonged use. Clinical studies have shown that motor-driven ULrrs significantly improve upper-limb Fugl-Meyer scores and daily living abilities in stroke patients [66, 68].

• Pneumatic-driven ULrrs: Powered by air pressure, these robots provide cushioning and adaptability, making them suitable for patients with severe spasticity. While less common, they have shown promise in enhancing sensorimotor cortex activation and functional hand motion [80,81,82].

• Elastic-driven ULrrs: Utilizing elastic elements, these robots offer safer and more compliant interactions, reducing the risk of injury during erratic movements or spasms. They are particularly effective in improving synergy-independent motor control of the shoulder/elbow and wrist/hand [83,84,85].

By integrating these driving mechanisms and designs, ULrrs offer a range of options tailored to the diverse needs of stroke patients, ultimately enhancing the effectiveness of upper-limb rehabilitation. Since there are numerous ULrrs worldwide, this section does not go into detail and summarizes representative ULrrs as shown in Table 2. The rehabilitation model of ULrr falls into the category of activity-based therapy [59], which promotes experience-dependent neuroplasticity and function, and can be combined with a variety of therapies to maximise the recovery process. Helen Rodgers et al. conducted a four-year multi-centre randomised controlled trial starting in 2014, and called robot-assisted training for the upper limb after stroke (RATULS) [3]. Participants in RATULS were patients between 1 week and 5 years after their first stroke, aged at least 18 years, with moderately or severely limited upper limb function, 770 in total. Results showed that the single ULrr did not improve upper limb function after stroke. Although single ULrr is not cost-effective, ULrr combined with motor learning has been shown to have potential for upper-limb motion function recovery [60]. In 2014 a randomised controlled trial demonstrated that ULrr combined with visual feedback contributed to the promotion of upper-limb motion recovery in subacute stroke patients [61]. Several studies have also shown that ULrr increases activation of the sensorimotor cortex [62], improves functional hand motion [62, 63], reduces interhemispheric inhibition [64], and improves motion [65] during the performance of rehabilitation tasks. Furthermore, several studies have systematically reviewed or meta-analysed the efficacy of ULrr-assisted therapy in improving upper-limb dyskinesia after stroke: ULrr-assisted therapy significantly improved Fugl-Meyer scores in the upper-limb of stroke patients compared with dose-matched conventional rehabilitation [66]; ULrr-assisted therapy improved synergy-independent motor control of the shoulder/elbow and wrist/hand [67]; and 45 analyzed trials (involving 1619 participants) were analyzed, yielding high-quality evidence that ULrr improves daily living ability, arm function, and muscle strength in stroke survivors [68].

Table 2 Representative Upper-limb rehabilitation robots

While upper-limb rehabilitation robots have demonstrated significant potential in enhancing motor recovery through repetitive, task-specific training, their effectiveness relies heavily on the ability to accurately monitor and interpret patient movement. This is where upper-limb motion sensing and metrics become indispensable. Motion sensing technologies, such as wearable sensors, inertial measurement units (IMUs), and surface electromyography (sEMG), provide real-time data on joint angles, muscle activation patterns, and force generation, enabling precise assessment of motor performance. These data not only guide robotic interventions but also offer quantitative metrics to evaluate rehabilitation progress and tailor training protocols to individual needs. In the following section, we delve into the principles, technologies, and applications of motion sensing and metrics, highlighting their critical role in creating a data-driven, adaptive rehabilitation framework that complements the capabilities of ULrr systems.

Upper-limb motion sensing and metrics

As shown in Fig. 2, upper-limb motion sensing and metrics are pivotal components of the closed-loop rehabilitation strategy. Motion perception involves the monitoring of upper-limb motion position, velocity, acceleration, surface electromyography (sEMG), and electroencephalography (EEG). These monitoring outcomes form the core of the ULrr control system, ensuring safety, facilitating human-computer interaction, and enhancing anthropomorphism to propel the rehabilitation process forward. Additionally, they serve as input signals for motion metrics and neural microfluidics. Upper-limb motion metrics are crucial for ULrr feedback control, enabling timely assessment of rehabilitation stages based on sensing. This is fundamental for achieving precise rehabilitation and personalized medicine. Furthermore, upper-limb motion sensing and metrics can deliver natural and humanoid electrical signals to neuronal microfluidics through processes of decoding, modeling, and translation.

Fig. 2

The essential role of upper-limb motion sensing and metrics in closed-loop rehabilitation after stroke. The green and orange arrows represent input and feedback, respectively. The outside and inside of the parallelogram are motion-sensing and metric technologies, respectively

Upper-limb motion sensing

Motion sensing technologies vary widely in their principles, applications, and clinical utility, each offering unique advantages and limitations. To provide a clear overview, Table 3 summarizes the key motion sensing methods. In the following sections, the article delves into the technical details, recent advancements, and clinical applications of these methods, emphasizing their integration into the closed-loop rehabilitation strategy.

Table 3 Summary of motion sensing methods

Motion capture systems are advanced technologies designed to track and record motion using various types of sensors [86]. Among the sensors frequently employed in academic research are depth cameras, optical sensors, and inertial sensors. Optical motion capture systems utilize a camera in conjunction with an infrared light source to precisely monitor motion. These systems calculate motion data, typically represented in 3D coordinates, by analyzing the positions of visual feature points or reflective markers [87, 88]. Due to their high accuracy and reliability, optical motion capture systems are widely regarded as the “gold standard” in the field [86]. Inertial motion capture systems, on the other hand, employ accelerometers, gyroscopes, and other sensors to measure acceleration and angular velocity. These systems are typically integrated into devices or wearable sensors known as inertial measurement units (IMUs) [89]. Despite the drawback of integral drift associated with IMUs, combining a triaxial flow sensor with an IMU mitigates this issue by eliminating the need for the integral link [87].

Optical motion capture systems, while highly accurate, are often redundant and expensive, making them more suitable for controlled experimental research rather than widespread application. In contrast, IMUs are lightweight and portable but are limited in the range of motion information they can capture. Depth camera-based motion capture systems present a compelling alternative, offering ease of deployment, affordability, and open-source flexibility, along with the ability to capture rich motion data. Commonly used depth cameras in such systems include the Kinect, Orbbec, Intel RealSense, and ASUS Xtion. In 2001, Microsoft introduced the first generation of Kinect cameras, capable of capturing real-time RGB and depth information [90]. Crucially, Microsoft later open-sourced the human skeleton recognition code, thereby pioneering a new research area in human motion sensing based on the human skeletal tracking framework [91,92,93]. In 2019, Microsoft released the Azure Kinect (3rd generation) with higher reliability and better position estimation than its predecessors [94]. The upper-limb motion sensing capabilities of the Azure Kinect are illustrated in Fig. 3. This device offers three depth camera modes: narrow-field-of-view (NFOV), wide-field-of-view (WFOV), and infrared mode (IR). Additionally, it can capture 15 key points of the upper limb.

Fig. 3

Upper-limb motion sensing in Azure Kinect. (a) NFOV depth mode. (b) WFOV depth mode. (c) Upper-limb skeleton and 15 keypoints (X, Y, Z) captured by Azure Kinect

Bioelectricity refers to the electrical signals or potential differences generated in living organisms, which are closely related to physiological functions. Common bioelectricities include cerebral electrical (measured as EEG), myoelectricity (measured as EMG), action potentials, intra/extracellular currents/potentials, and organ currents, among which EEG and EMG are closely related to upper-limb motion. In recent years, Innovations and advances in bioelectronics have been crucial drivers for the advancement of EEG- and EMG-based motion sensing [95]. Such advances will provide a more complete chain of information from brain to muscle, providing critical support for the design and optimization of rehabilitation strategies.

EMG-based motion sensing, typically performed on the skin surface, plays a pivotal role in stroke rehabilitation by capturing motion-related muscle electrophysiological signals. These signals are essential for assessing muscle activation patterns, detecting abnormalities such as spasticity or co-contraction, and monitoring the progress of motor recovery. As illustrated in Fig. 4(a), conventional sEMG sensors utilize electrode materials designed to maintain stable electrode-skin interface impedance [96,97,98]. These electrodes, often circular or rectangular in shape with diameters ranging from 10 to 20 mm, are strategically placed based on the anatomical location of the target muscle to ensure optimal signal acquisition [96, 99]. The precise positioning of electrodes is critical, as it directly impacts the quality and reliability of the data collected [97, 100, 101]. Furthermore, the use of multiple electrodes can significantly enhance signal quality and provide more comprehensive information, enabling a detailed analysis of muscle activity during rehabilitation exercises [101]. As such, it is common to employ 2 to 8 electrodes to cover the muscle being tested [98, 101].

Fig. 4

Bioelectricity-based motion sensing electrodes. (a)-(d) sEMG electrodes. (e)-(h) EEG electrodes. (a) Traditional sEMG electrode. (b) Large tattoo-like electrode from Youhua Wang et al. [103] (c) Organic electrodes based on topological supramolecules from Yuanwen Jiang et al. [105] (d) Metal-polymer electrode array patch from Shuaijian Yang et al. [106] (e) Traditional EEG electrode. (f) Dynamically cross-linked hydrogel electrode from Qinhua Wang et al. [135] (g) Conductive biogel electrodes applied on the skin, from Chunya Wang et al. [112] (h) Multichannel polydopamine nanoparticle hydrogel electrode from Qingquan Han et al. [113]

In recent years, breakthroughs in bioelectronics [102] have led to the development of innovative sEMG sensors that are revolutionizing stroke rehabilitation by enhancing the monitoring and analysis of muscle activity during upper-limb recovery. For instance, in 2020, researchers at Huazhong University of Science and Technology introduced a groundbreaking large-area, soft, breathable, and substrate-less sEMG sensor [103]. This tattoo-like electrode, designed for large-area epidermal electronics, offers unprecedented comfort and precision, making it ideal for long-term use in stroke patients undergoing rehabilitation. In 2021, a team at Stanford University [104] advanced the field further by creating strain-insensitive, intrinsically stretchable transistor arrays capable of capturing high-quality sEMG signals from human biceps, even during dynamic movements. This innovation is particularly valuable for stroke rehabilitation, as it allows for accurate monitoring of muscle activity during repetitive exercises. Building on this progress, Stanford researchers developed a highly conductive, soft, and stretchable organic material in 2022, based on a topological supramolecular network [105]. This material has demonstrated exceptional performance in monitoring myoelectric activity, not only in human arms but also in complex biological systems, highlighting its potential for precise and adaptable applications in stroke recovery. Recently, in 2023, a collaborative effort between the Southern University of Science and Technology and the University of Leeds [106] yielded an adhesive dry electrode and metal-polymer electrode array patch. These new electrodes outperformed commercial sEMG sensors in experimental trials, showcasing their superior signal quality and reliability in motion-related applications. Together, these advancements in sEMG sensor technology are transforming stroke rehabilitation by enabling more accurate, comfortable, and long-term monitoring of muscle activity, ultimately supporting personalized and effective recovery strategies for upper-limb motor function.

On the other hand, there are EMG electrodes that work inside the body (iEMG). Although iEMG allows for clearer information and a wider field of view, the invasive electrodes can cause physical and mental damage to the tester [102]. Therefore, sEMG is favoured over iEMG electrodes, while sEMG electrodes can also provide valuable insights into the status of human muscle activity. The materials, structural design, biocompatibility, and applications of sEMG electrodes are beyond the scope of this review and can be found in the reviews by Lian Cheng [102], Ying Chen [107], Xu Hao [101], and Bi Luzheng [108] et al.

EEG-based motion sensing plays a transformative role in stroke rehabilitation by capturing brain activity information through electronic devices integrated with the human body, enabling a deeper understanding of neural mechanisms and facilitating targeted interventions for upper-limb motor recovery. EEG sensors, categorized into consumer-grade [109, 110], medical-grade [110, 111], and experimental-grade [112,113,114,115] types, have evolved significantly over the past century. Up to 2017, medical-grade EEG sensors, in particular, have demonstrated superior data quality, reliability, and analytical depth compared to consumer-grade alternatives [110], making them indispensable for clinical applications in stroke rehabilitation. As of 2024, EEG technology, often affectionately referred to as the “old dog” by scholars [116], has reached unprecedented levels of sophistication, driven by advancements in materials science, signal processing, and miniaturization. Modern EEG electrodes, as illustrated in Fig. 4(e), are designed to be more comfortable, precise, and adaptable, enabling long-term monitoring of brain activity during rehabilitation exercises. These innovations allow clinicians to track neural plasticity, decode motor intentions, and provide real-time feedback through brain-computer interfaces (BCIs), which can drive robotic exoskeletons or functional electrical stimulation devices. By integrating EEG sensing into stroke rehabilitation protocols, therapists can tailor interventions to individual neural patterns, optimize recovery outcomes, and ultimately restore upper-limb motor function more effectively.

Wet Ag/AgCl electrodes, widely regarded as the “gold standard” for EEG [115], have traditionally been used to capture high-quality brain activity signals through wearable arrays positioned at specific scalp locations [117,118,119,120]. These electrodes, often adhered using water-soluble adhesives [121], are guided by international standards such as those recommended by the American Academy of Clinical Neurophysiology [122] (with additional placement guidelines provided by Margitta Seeck et al. [118]). While effective in clinical settings, this method is often impractical for continuous monitoring in natural daily environments, limiting its utility for stroke patients undergoing long-term rehabilitation. To address this challenge, innovative EEG sensor designs have emerged, such as ear-worn devices integrated into headphones [123,124,125], which enable discreet and comfortable monitoring of brain activity during everyday tasks. Additionally, advancements in textile-based dry EEG electrodes have introduced a promising alternative for long-term measurements, offering benefits such as skin-friendliness, lightweight design, flexibility, and washability [126, 127]. These features make textile electrodes particularly suitable for stroke rehabilitation, where prolonged and non-invasive monitoring is essential to track neural plasticity and motor recovery progress. However, despite their potential, the practical application of textile dry EEG electrodes requires further investigation, as quantitative comparisons with conventional wet electrodes are still lacking [126]. By overcoming these limitations, next-generation EEG sensors could revolutionize stroke rehabilitation by providing continuous, real-time insights into brain activity, enabling personalized and adaptive interventions to restore upper-limb motor function more effectively.

EEG sensors have undergone significant advancements to address challenges in stroke rehabilitation, particularly in enabling long-term, high-quality monitoring of brain activity to support upper-limb motor recovery. Traditional EEG recordings rely on gels or pastes to ensure reliable skin-electrode contact [107, 111,112,113,114,115, 128]. However, these materials often struggle to maintain conformal contact with the hairy scalp [129, 130] and are prone to drying out over time [125, 131], leading to signal deterioration during extended use [132, 133]. To overcome these limitations, researchers have developed innovative alternatives, such as electrolyte-permeable materials with porous structures [133], soft hydrogels with tissue-like mechanical properties, and hydration-effective interfaces [128,129,130, 134]. Since 2020, breakthroughs like dynamic crosslinked hydrogel/coating [135], non-drying hydrogel electrodes integrated into textiles [136], on-skin paintable conductive biogels [112], homeostatic biosensors with porous cellulose membranes [137], and podopamine nanoparticle hydrogels [113] have demonstrated exceptional EEG signal capture capabilities, ensuring stable and high-fidelity recordings even during prolonged rehabilitation sessions. Additionally, dry and semi-dry electrodes have emerged as a promising solution for stroke rehabilitation, offering greater comfort and usability. Innovations such as flexible large-area polymer-based arrays [138], stretchable dry electrodes [115], printable Ag/AgCl dry electrodes [139], ultrathin dissolvable silk fibroin electrodes [140], and high-density silicon nanomembrane transistor arrays [141] have expanded the possibilities for EEG monitoring. While dry/semi-dry electrodes are particularly effective for hairless skin regions due to lower impedance and noise [142], their development represents a significant step toward more patient-friendly EEG technology. These advancements are especially critical for stroke rehabilitation, where continuous and reliable EEG monitoring can provide real-time insights into neural plasticity, motor intent, and recovery progress.

Upper-limb motion metrics

Upper-limb motion metrics involve the quantitative measurement and assessment of motion, which is a crucial step following sensory data acquisition [143, 144]. The primary objective of these metrics is to record and analyze various characteristics of motion, including joint movement information, upper-limb structural parameters, and motion patterns [144]. These features offer an objective evaluation of the quality, efficiency, functionality, and patterns of upper-limb motion, which is essential for monitoring the rehabilitation process after stroke [145]. As shown in Fig. 2, in the closed-loop strategy elaborated in this review, the metric results can be used as feedback and input for rehabilitation robotics and neural microfluidics, respectively. Table 4 summarises commonly used upper-limb motion measures.

Scales. Traditionally, upper-limb motion metrics after stroke have been accomplished through established clinical assessments (scales). Scale-based metrics research dates back to the 1960s [146]. Although the long history of scales provides a rapid clinical assessment, their subjective judgements fail to provide a precise metric. Most importantly, scales cannot be involved in automated processes and thus fail to work in concert with computers, robots, and physicians, and are a primitive form of metrics.

Kinematic metrics enable sensitive quantification of the upper-limb motion quality. Additionally, the motion capture system used in upper-limb motion sensing can provide extensive kinematic information, which serves as a robust foundation for implementing kinematic metrics [147]. Since 1990, the International Society of Biomechanics (ISB) has been working to provide uniform standards for human kinematics to standardise joint motions and kinematic descriptions [148, 149].

In 2019, Anne Schwarz et al. investigated in detail 225 studies related to upper-limb motion metrics after stroke [145]. The investigation results showed that 36% of the studies were 2D pointing tasks, 7.1% used 2D shape drawing tasks, 29.8% were based on 3D pointing tasks, 22.2% used 3D stretching and grasping tasks, and 10.7% belonged to other types. Also summarised were 151 different kinematic indices to quantify upper-limb motion function. Besides the direct metrics described above, there are also indirect metrics through motion recognition, where the system inputs are likewise kinematic parameters. The methods recognised for use in upper-limb motion metrics are angles [150], motion patterns [151, 152], motion representation [152], the length and orientation of the bones [153], etc. Other kinematic-based metrics are shown in Table 4.

Signal-based metrics are mainly quantitative analyses, ratings, and pattern recognition through sEMG and EEG. Stroke patients can exhibit abnormal upper limb muscle manifestations such as decreased myodynamia, irregular muscle contraction times, and reduced synchronization [3, 154,155,156]. These manifestations occur at the muscle level, making metrics based on sEMG signals particularly effective in directly reflecting muscle conditions. However, upper-limb motion involves multiple tissues, including muscles and bones, and the central nervous system (CNS) faces significant challenges in independently controlling individual DOF.

To address this complexity, Muscle Synergy Theory (MST) introduces the concept of hierarchical motor control, where the CNS simultaneously recruits and controls groups of muscles in a modular manner, facilitating motor behavior [155, 157]. MST is considered a fundamental strategy to simplify motor behaviour and reduce control redundancy within the CNS [158]. Consequently, numerous studies have focused on motion metrics derived from MST and sEMG in recent years, as summarized in Table 3. Meanwhile, machine learning has been widely applied in upper limb motion pattern recognition based on sEMG signals, such as 8-channel incremental learning deep learning methods [159], Ahead-sEMG adaptive detection [96], Continuous upper-limb motion estimation based on CNN-LSTM models [160], and bi-directional LSTM Network [161].

EEG signal-based metrics are complex, high-dimensional, highly noisy, non-linear, and non-Gaussian [162,163,164]. EEG characteristics are highly influenced by various individual differences such as age and psychology [164, 165]. Moreover, EEG signals are susceptible to noise sources such as biological artefacts, electronic devices (wireless devices, mobile phones, and computers), and environmental noise [163]. These challenges make the interpretation, metrics, and classification of EEG signals a difficult task [166, 167]. Advanced machine learning and deep learning algorithms for processing and decoding complex and large EEG data are the mainstay of current research. A detailed discussion of the algorithms is beyond the scope of this review, and references can be found in the reviews by Hamdi Altaheri [163], M-Parsa Hosseini [167], Zitong Wan [164], F Lotte [165], Alexander Craik [168], and Xiang Zhang [169] et al.

Quantitative studies are an effective modality to metricise upper-limb motion based on EEG [170]. Quantitative event-related analyses demonstrated that the magnitude of high-mu and low-beta event-related desynchronization (ERD) in subacute stroke patients was highly correlated with residual upper-limb motion capacity [171]. A study involving 29 patients with a first unilateral stroke showed a significant correlation between the degree of upper limb stroke injury and the intensity of ERD or event-related synchronisation (ERS) [172]. Similarly, a mirror-image visual feedback study revealed a relationship between the amount of beta ERD attenuation and upper-limb dyskinesia [173]. In addition, quantitative analyses based on muscle synergy analysis, spectral power, symmetry, functional connectivity, and rhythmic are valid tools for the metrics. Besides quantitative studies, the estimation of kinematics and kinetics from EEG has also demonstrated metric potential. In decoding studies of hand grasping patterns, nonlinear autoregressive model that predicts fingertip forces can use force as a metric. Studies decoding upper-limb motion from EEG allow the use of motion execution as a metric.

Table 4 Upper-limb motion metric measures

Model-based metrics are parameterised and mathematically modelled for the upper limb, and such models offer the possibility of accurately quantifying upper-limb motion through mathematical means. But, because of the highly redundant and complex kinematics and dynamics, as well as the limitations of the musculoskeletal system, modelling the upper limb is a challenging task [88]. That is why there are relatively few model-based metrics studies. In 1994, DAVID HESTENES was the first to use geometric algebra to formulate and analyse upper-limb kinematics [200]. This research pioneered upper-limb analysis and modelling based on geometric motion. In 2008, Sigal Berman et al. modelled the rigid-body motion of the humerus and forearm based on 4D degenerate geometric algebra (Motor algebra), while investigating the decomposition and reconstruction of the motion [201]. In 2019, Dario Pavllo et al. proposed QuaterNet, a neural network architecture based on quaternions [202]. This architecture parameterises rotational motion of the upper limb and offers advantages in both short-term prediction and long-term generation. In 2020, Robert P. Matthew et al. enhanced the biological feasibility of restoring upper-limb motion based on a rigid body modelling approach [203]. The most recent research on upper-limb modelling is the P-BTBS proposed in 2023, which is based on geometric spatial modular upper-limb motion and establishes a Triangular Primitive Space (TPS) and then maps upper-limb motions in Euclidean space and TPS to each other in a novel way, and finally completes the modelling in both spaces [88].

Upper-limb modeling is a mathematical approach designed to describe upper-limb motion. The primary purpose of modeling is to create an accurate virtual model to simulate and analyze upper-limb motion behavior in various situations. Conversely, the primary goal of metrics is to quantify the performance and functional state of upper-limb motion. Modeling provides the theoretical framework and predictive tools, while metrics offer the actual data and means of validation. The integration of these two elements enables more precise and effective upper-limb motion analysis and interventions, ultimately advancing the understanding and application of upper-limb motion function. Specifically, model-based metrics exhibit the following five characteristics: (1) Validation and Calibration, upper-limb motion metrics can validate and calibrate the motion model, enhancing its accuracy and reliability. (2) Comparative Analysis, the predicted motion behaviors of the model under different conditions can be compared with actual metric data, thereby assessing the model’s validity and utility. (3) Personalization, metrics can obtain individual-specific motion data to create a personalized motion model for each person. This is especially crucial in rehabilitation therapy, where a tailored rehabilitation plan can be developed based on the patient’s specific condition. (4) Effectiveness Evaluation, the model can simulate different rehabilitation training methods, while the metric data verifies which method is the most effective. (5) Real-time Adjustment, real-time metric data can dynamically adjust the model, providing immediate exercise analysis and feedback to enhance the effectiveness of exercise training and rehabilitation.

Neuronal microfluidics and electronics

In recent years, the synergistic development of neuroelectronic devices engineering, microcircuit engineering, and microfluidics has ushered in a new era of “brains on a chip” [204,205,206,207,208].

Neuronal microfluidics is the final step in the closed-loop rehabilitation discussed in this review and serves as a strong indication of the rehabilitation effect. Neuronal microfluidics is expected to provide personalised treatment protocols that test the effectiveness of treatment by mimicking the patient’s neurological conditions. Specifically, metrics following upper-limb motion sensing can provide neuronal microfluidics with natural and humanoid electrical stimulation signals. Meanwhile, the data from the motion metrics can be used to adjust the parameters of the electrical stimulation to optimise the rehabilitation training effect. Electrical stimulation, in turn, can activate or inhibit specific neural pathways that affect motion perception. This section will illustrate the importance of neuronal microfluidics starting with a discussion of electrical stimulation.

Clinical significances of electrical nerve stimulation

Electrical stimulation is a routine treatment in the field of rehabilitation medicine and is clinically important in the rehabilitation of upper-limb dyskinesia after stroke [209, 210]. Electrical stimulation is mainly divided into two types: Functional electrical stimulation (FES) and Neural Electrical Stimulation. FES is the application of electric current to muscles to induce contraction to simulate natural motion and is often used to restore specific functional motions. Although FES could indirectly influence neural reorganization through repeated movement practice and sensory feedback, its core mechanism operates at the muscular level, rather than directly modulating neural activity or plasticity. Neural electrical stimulation improves neural response using electrical current, which promotes neuroplasticity and enhances neural connectivity [210, 211]. In combination with other rehabilitation tools, electrical stimulation can significantly improve the quality of life and independence of patients [212, 213]. This section focuses on neural electrical stimulation to explore the restorative value of neuronal microfluidics after integrating current. The history, principles, physiology, and clinical applications of electrical stimulation are beyond the scope of this review and can be found in McCaig, C. D. et al. [214].

Neural electrical stimulation, particularly vagus nerve stimulation (VNS), has emerged as a powerful tool for improving motor performance and promoting neural recovery in stroke rehabilitation. VNS, which has been studied for over a century [210], has gained significant attention in recent years for its potential to enhance upper-limb motor function in stroke patients. A landmark controlled trial led by Jesse Dawson et al. in 2017, conducted across 19 stroke rehabilitation centers in the UK and US [213], demonstrated the therapeutic benefits of combining VNS with rehabilitation for patients with upper-limb dyskinesia after ischemic stroke. By 2019, the trial included 106 participants, with results showing significant improvements in arm function and long-term compliance, underscoring the clinical potential of VNS. Similar studies such as controlled trials and follow-ups have reached the same conclusion: vagus nerve stimulation has good long-term compliance and may improve arm function after ischaemic stroke [212, 215]. The neural mechanisms underlying these improvements have been further elucidated through animal studies. For instance, Lindsay Collins et al. (2021) used a mouse model to show that VNS elicits widespread cortical activation and drives behavioral recovery, highlighting its ability to modulate neural plasticity [216]. More recently, Xiao-mei Xia et al. (2024) investigated the neuroprotective mechanisms of VNS in a mouse model of ischemic stroke, revealing its role in reducing neural damage and promoting functional recovery [217]. These findings collectively demonstrate that VNS enhances motor performance by facilitating neural repair, modulating cortical activity, and promoting adaptive plasticity. The therapeutic potential of VNS is further supported by its inclusion in the International Consensus-Based Review and Recommendations (Version 2020) [218], which provides a comprehensive overview of its applications and mechanisms. This approach aligns with the growing emphasis on neuromodulation as a key strategy for enhancing neural repair and functional restoration in stroke patients.

Transcranial electrical stimulation (TES) represents a direct and effective approach to modulating neural activity, with demonstrated potential to improve motor performance and facilitate upper-limb recovery after stroke [210, 219, 220]. TES works by generating electric fields that penetrate deep brain structures, such as the amygdala, hippocampus, and cingulate gyrus [221], inducing local currents that electrically stimulate targeted brain tissues [211]. This neuromodulation enhances cortical plasticity, a key mechanism underlying motor learning and functional recovery. The therapeutic benefits of TES have been consistently supported by experimental and clinical studies. For example, a 2021 single-blind crossover study by Shashi Ranjan et al. involving 12 patients with chronic hemiplegia demonstrated that cerebellar TES significantly improved postural control, highlighting its potential to restore motor function [222]. In 2022, a dual-center, double-blind, randomized clinical trial further validated the efficacy of combining TES with upper-limb robotic training, showing that patients with corticospinal dysfunction experienced notable improvements in upper-limb motion function [223]. Additionally, a 2023 open-label, non-randomized phase I trial by Kenneth B. Baker et al. involving 12 post-stroke patients confirmed the safety and feasibility of TES as a promising intervention for upper-limb functional recovery [6]. Meta-analyses and reviews in recent years have further reinforced the positive impact of TES on upper-limb motion recovery after stroke [224,225,226], emphasizing its role in promoting nerve cell repair and neural network reorganization. By modulating neural excitability and enhancing synaptic plasticity, TES not only improves motor performance but also addresses the underlying neural deficits that contribute to post-stroke motor impairments. These findings underscore TES as a valuable tool in stroke rehabilitation, offering a non-invasive and targeted approach to driving neural recovery and functional restoration.

Neuronal microfluidics (NMs)

Over the past decade, the rapid advancement of microfluidics has revolutionized neuroscience research by enabling the precise assembly of neural cells and circuits, while also providing sophisticated platforms that can complement and, in some cases, replace traditional animal models [227]. Neuronal microfluidics (NMs), a specialized application of microfluidics, involves the design and fabrication of devices with microscale channels and chambers that mimic the structural and functional complexity of the brain. These devices allow researchers to monitor, manipulate, and interact with individual neurons or neural networks in a controlled environment, supporting high-throughput automation and the parallel execution of multiple experimental tasks [228, 229]. NMs are typically structured in two main configurations: 2D and 3D. Both designs aim to replicate the in vivo features of neural network formation, such as cell-cell interactions, axonal guidance, and synaptic connectivity [230]. Notably, 2D microfluidic devices integrated with electronic components, such as microelectrodes and microcircuits, enable simultaneous recording and stimulation of neural activity, providing a powerful platform for studying neural dynamics and plasticity [206]. Furthermore, the integration of microfluidics with optogenetics—a technique that uses light to control genetically modified neurons—has significantly enhanced the ability to precisely stimulate neurons and modulate neural circuits [231,232,233]. This combination allows for spatiotemporal control of neural activity, offering unprecedented insights into the mechanisms underlying neural repair and rehabilitation.

The organic integration of NMs with electrical stimulation holds strong potential for advancing stroke rehabilitation research. By replicating the neural microenvironment and enabling precise control over neural activity, NMs can be used to model stroke-induced damage and test therapeutic interventions. For example, stroke models created using NMs can simulate ischemic conditions, allowing researchers to study the effects of electrical stimulation on neural repair, axonal regeneration, and synaptic reorganization. These models provide a platform to explore how electrical stimulation can enhance neural plasticity and promote functional recovery in stroke patients.

In this section, we focus on three key aspects of NMs relevant to stroke rehabilitation: physiological models, stroke models, and circuit design. These components are critical for understanding the mechanisms of neural repair and developing targeted rehabilitation strategies. By leveraging the capabilities of NMs, researchers can gain deeper insights into the neural mechanisms of stroke recovery and develop innovative [207, 234], personalized rehabilitation therapies that harness the power of electrical stimulation and neural circuit modulation.

Physiological modeling

Physiological modelling of NMs involves structures, microenvironments, and biomaterials. Controlling the physiological modelling process allows the development of NMs for different functions, e.g., cell manipulation, axon culture, electrophysiological testing, and in vitro stroke modelling.

Different NMs structures allow the construction of nerve cells and circuits in spatially and temporally controlled environments. As shown in Fig. 5, several studies have explored the effects of groove [235], axonal diodes (directional microchannels) [236], geometry of the microfluidic channels [237], and chambers [238,239,240] on nerve cell growth [235], nerve fibre alignment [236], axon growth [237, 240], axon segregation [238], and axonal connection [239]. Recent studies have also explored the benefit potential of hydrogel and collagen for neuronal cell [241]. Specifically, neurons developing in vitro undergo sequential differentiation, polarisation, and specialisation of axons or dendrites, in which axonal growth represents the state of the nerve cell [242, 243]. Axons can grow along microfluidic channels, so different cellular mechanisms can be investigated by isolating axons from the cell body through different geometries [236, 237, 244]. In addition, multi-chambered NMs can guide axonal growth and neuronal connections, such as two-compartment [239, 240], three-compartment [245, 246], and multi-compartment [247]. Among these, two- compartments are commonly used for neuronal cell cultures, e.g., inoculation of neurons in two compartments allows for the generation of bi-directional networks of connections, and axon segregation; some studies add a third compartment for culturing different kinds of neurons or manipulating isolated axon branches; and multi-compartments are frequently chosen for the connection of different neuron populations, which allows for engineering complex circuits and testing compounds in a specific population. Especially multi-chambered NMs, combined with high spatiotemporal resolution optogenetic techniques have been observed for stimulus conduction in cortical neurons [247]; assembled with azobenzene-containing molecular glass films (containing photolithographic nanomorphology) can effectively guide axon growth [248]. There are also NMs for single neuron analysis, which can be found in the review by Pallavi Gupta et al. [249].

Microfluidics facilitates the design of quantitatively complex microenvironments that include multiple control parameters such as flow rates, factors concentration, sheer stress, spatiotemporal gradient cues, nutrient ratios, and delivery times of biochemical reagents [227, 250,251,252]. It has been shown that restrictive microenvironments promote neuronal growth and maturation, such as different channel cross-sectional areas [248] and poly-D-lysine [240]. Neuronal chemotaxis was demonstrated by molecular gradients generated with large arrays of hydrogel cylinders, demonstrating the different roles of the microenvironment in the regulation of chemotaxis [241]. Addition of Wnt3a to microfluidics with symmetrical chambers promotes differentiation of hpNPC into DG and CA3 neurons and recapitulates the connectivity between the two types of neurons [239]. Composite microfluidics consisting of one-dimensional sequences and two-dimensional arrays can generate a large coding volume of fused brain-like neurons and demonstrate good growth of cortical, hippocampal, and thalamic neurons [253]. It has been demonstrated that tension formed through NMs induces the growth of neural synapses [254]. Interestingly, to counter the drawback that NMs require a large number of neurons for inoculation, Georg Jocher et al. describe a simple method of inoculating and culturing neurons that drastically reduces the number of neurons required per NMs to 10,000 neurons [255].

In vitro stroke modelling based on NMs

Section 1.2 (Characteristic responses of neure after stroke) systematically discusses a range of post-stroke neuronal cell responses such as injury, apoptosis, autophagy, and ferroptosis, with particular reference to changes in intra/extracellular ATP, ions, proteins, etc. These responses and changes are passive processes after stroke onset, and an in vitro stroke model based on NMs is an active simulation to rescue nerve cells. Advanced in vitro models are gradually replacing traditional animal models through physiological modelling allowing the reproduction of patient-like pathophysiological characteristics [205, 208, 264]..

Fig. 5

Neuronal microfluidics (NMs). (a)-(c) Two-compartment NMs. Similarly reported by Anne M Taylor [256], Jeong Won Park [257], and Joyce W. Kamande [240] et al. (d)-(g) Multi-compartment NMs. (h) and (i) Innovative material NMs. (a) Modelling of neuronal connections within the hippocampus [239]. (b) Axonal diodes NMs that allow directed neuronal connections [236]. (c) Deposition chamber-based NMs that mimic physiologically relevant neural structures [258]. (d) NMs with high temporal resolution, with fast on/off kinetics, and allowing for multiple microenvironments [246]. (e) NMs capable of building multi-node neural networks [259]. (e-1) Immunofluorescence images of Tuj-1 (green) and F-Actin (red) staining (middle); fluorescence images of Tuj-1 (green) and Hoechst (blue) staining (right) [259]. (f) and (g) NMs with temporally and spatially controllable chemical gradients [260] and NMs for secondary diffusion [261]. (h) 3D printed NMs containing hydrogel microcapsules of cultured and differentiated neural cells [262]. (h-1) DAPI staining of nuclei (left) and tubulin subunit beta3 staining of mature neuromasts (right) [262]. (i) Biohybrid NMs using 3D micromachining with two-photon lithography [263]. (i-1) F-Actin staining (red) of bEnd.3 cells (left) and high magnification SEM images (right) [263]

The strength of NMs lies in their ability to accurately replicate a wide range of physiological conditions, multicellular cell types, and cell-cell interaction environments, and in particular the integrated electronics provide real-time monitoring regulated by different biochemical and biophysical signals [264]. Meanwhile, NMs allow replication of critical elements of each cell to develop creative experimental designs, such as microenvironments with temporal-spatial controllability, continuous medium flow, biochemical cues, and complex interactions between cells [265]. In practice, NMs have been applied in studies of neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s chorea, etc.) [264, 266], traumatic brain injuries [260, 266, 267], axonal damage [256, 267,268,269], and neuroinflammation [261, 270]. This section aims to emphasise studies that establish in vitro models of stroke, thereby advancing substantial breakthroughs in neuroprosthetics and stroke rehabilitation.

Decade ago, there were fewer studies related to the establishment of in vitro stroke models based on NMs, with only around 1/1000 of the identified stroke studies [271]. One of the primary material changes in stroke is oxygen, so precise temporal and spatial control of oxygen [272, 273], even gradient control [274], is routinely modelled. Besides providing a controlled supply of oxygen, NMs allow more detailed modelling of neuronal cell pathological responses, e.g., excitotoxicity [36, 56] and axonal injury. Multi-chamber NMs culturing hippocampal neurons and establish synaptic connections to deliver excitotoxic damage have been studied [261]. This study also demonstrated that excitotoxicity is driven by GluN 2B receptors and identified a GluN 2 A-dependent neuroprotective signalling mechanism. Anne M Taylor et al. reported direct and reproducible NMs that polarise axons into isolated environments without targeted neurotrophic factors, which in turn can be used in studies of axonal injury, regeneration, and transit [256]. Many methods have been developed to simulate axonal mechanical injury in vitro, including laser microdissection systems [275], sharp metal blades [276], glass electrodes [277], rubber impingers [278], bubble [269], hydrostatic pressure [238], and vacuuming [256, 279]. After inducing axon growth and detachment in NMs, laser pulse energies of 400 nJ and 800 nJ have been demonstrated to result in partial and complete transection of axons, respectively [268]. On the other hand, many different agents have been used in NMs to destroy nerve cells in a precise spatio-temporal manner, e.g., saponin induces Wallerian degeneration of distally damaged fibres (degeneration caused by damage to axon terminals) [280]; manganese induces axotoxicity and axon disintegration [281]; sodium dodecyl sulphate induces damage to individual neurons [282]; and acrylamide induces degeneration [283].

Recent years have seen the gradual development of beneficial foundations and advances in stroke modelling with the advancement of technologies such as NMs, biofabrication, and micromanipulation. Another material change after stroke is a dramatic decrease in glucose, and experiments based on NMs for glucose deprivation have been shown to be feasible [248]. Similarly, NMs combined with hydrogels can produce highly controllable three-dimensional biochemical gradients [241, 260]; NMs simulating basal ganglia circuits elucidate the effects of excitotoxicity on network activity [284]; precise dosing of neurotrophic factors in multi-chambered NMs promotes regeneration after axonal injury [255]; Oxygen transport gradients can be controlled by chemical reactions that consume or produce oxygen [285]; these studies have demonstrated the potential to mimic the post-stroke microenvironment by controlling the supply of biochemicals. Inducing mechanical damage to axons by creating shear stress in microchannels through different physical processes is a commonly used method in recent years, such as flow rate [267], vacuuming [245], and air jet [286]. Furthermore, advances in new microfluidic technologies facilitate the construction of highly controllable microenvironments, such as open-channel microfluidics (precise localisation and limitation of fluid volume) [287] and modular NMs (multimodal cell culture) [288]. Notably, in 2021, Zhonglin Lyu et al. reported an NMs for simulating ischemic stroke that established a functional blood-brain barrier and simulated the ischemic penumbra by controlling oxygen, serum, and glucose supply [289]; the same year, Nienke R. Wevers et al. simulated ischaemic stroke by chemical hypoxia, hypoglycaemia and halted perfusion on a platform capable of culturing 40 NMs in parallel [290].

NMs platform offer a unique tool for investigating mechanisms of nerve injury and repair by simulating the post-stroke neural microenvironment, ultimately translating into measurable rehabilitation outcomes for patients. Firstly, these microfluidic devices can precisely replicate post-ischemic biochemical conditions, such as hypoxia and inflammatory factor concentrations, while enabling real-time monitoring of neuronal electrical activity through integrated microelectrode arrays. This capability allows researchers to evaluate the efficacy of various rehabilitation strategies, including electrical stimulation and pharmacological interventions, on neuronal cell survival and synaptic plasticity. Secondly, by converting patient-specific upper limb kinematic data into electrical signals and inputting them into the microfluidic platform, researchers can model individualized neurological injuries and predict the outcomes of tailored rehabilitation programs. Finally, experimental findings from the microfluidic platform, such as the extent of neuronal regeneration or improvements in network synchronization, can be directly applied to clinical practice. These insights enable physicians to refine rehabilitation protocols, thereby enhancing patients’ motor function and quality of life. This translational pipeline from laboratory research to clinical application not only accelerates the optimization of rehabilitation strategies but also establishes a scientific foundation for precision medicine in stroke rehabilitation.

However, the widespread dissemination of Microfluidic devices faces several challenges and limitations. Firstly, the fabrication and operation of these devices require highly specialized techniques and equipment, which are often costly and may not be accessible to resource-limited healthcare settings. Secondly, while microfluidic platforms can accurately replicate the neural microenvironment, their intricate designs and miniaturized structures may restrict their adaptability to dynamic, multi-scale physiological conditions. Additionally, the translation of microfluidic technologies from laboratory research to clinical practice necessitates addressing critical barriers related to standardization and reproducibility, including ensuring consistent device performance and precise control over experimental conditions. Moreover, the long-term stability and biocompatibility of microfluidic devices require further validation to guarantee their safety and efficacy in clinical use. Despite these challenges, interdisciplinary collaboration and ongoing technological innovations are expected to overcome these limitations, positioning microfluidic devices as an indispensable tool in the future of stroke rehabilitation.

Electrophysiology of NMs (ES-NMs)

The ES-NMs concept was first introduced in the late 1990s but without microelectrodes [291]. With the development of science and technological advances, electrophysiological studies have become the foundation of neuroscience research while serving as a beneficial instrument for stroke rehabilitation [207, 209]. In particular, ES-NMs measure, record and stimulate neurons by integrating electrodes and circuits, which provides valuable information about the behaviour of neurons and their networks. The combination of microfluidics and optogenetics further enhances the ability of ES-NMs to observe and modulate neurons and their circuits [206, 231, 232]. As shown in Fig. 6, ES-NMs will be discussed in this section in terms of both active (electrical stimulation) and passive (measurement and recording) forms, respectively.

Fig. 6

Electrophysiology of neuronal microfluidics (ES-NMs). The black circle and the black line in the figure indicate microelectrodes and microcircuits, respectively. (a)-(e) Measurement and recording of 2D structures. (f)-(j) Measurement and documentation of 3D structures. (k)-(q) Electrical stimulation of nerve cells in different paradigms. (a) Individual microelectrodes record one nerve cell electrophysiology [293, 294]. (b) Multiple microelectrodes measure axonal electrophysiology [297, 309]. (c) Microelectrode arrays record electrophysiology of neuronal networks [296,297,298, 300, 309]. (d) Microelectrodes record the post-salient signal (muscle or next neural protrusion) [299, 307]. (e) Microelectrode arrays for long-term recording of nerve cell electrophysiology [275, 295, 296]. (f) Flexible bendable probes with microelectrodes [301]. (g) Multi-channel 3D microelectrode arrays [302]. (h) Multifunctional (optical channels, drug flow channels, microelectrodes, etc.) microprobe [231, 291, 303]. (i) Hollow nanoantennas for electrophysiological measurements and simultaneous molecular delivery [292]. (j) Semiconductor-based neuronal microfluidics with high temporal and spatial resolution, capable of recording electrophysiology at the single-cell and network level [227, 305, 306, 308, 310, 317]. (k) MEAs can simultaneously record (circles) and stimulate (squares) nerve cells [299, 314, 315] and even enable spatiotemporally encoded electrical stimulation [318]. (m) Stimulation of nerve cells by vertical electric fields [314]. (n) The electric field strength varies with the width of the microfluidic channel [319]. (o) The resistive step design of the microfluidic control produces three orders of magnitude of electric field strength [320]. (p) Graphene [321], conductive nanofibres [322,323,324,325], and carbon nanotubes [326] are biocompatible while providing electrical stimulation of nerve cells. (q) An electrical niche platform composed of conductive graphene scaffold (CGS) and indium tin oxide (ITO) [327]

Extracellular measurement and recording

Extracellular monitoring is deployed by integrating circuits in direct contact with the nerve cells on microfluidics, such as microelectrodes, probes, and semiconductors. The application of microelectrodes, especially microelectrode array (MEA), to NMs realises a wide range of academic and engineering value including culture-monitoring, circuit/network connectivity, and biochemical manipulation [207]. Specifically, MEA can be used for non-invasive extracellular electrophysiological recording and action potential evocation, representing a unique electrical interface without cell damage [207, 292]. And MEAs are easy to fabricate, with scales and circuits suited to the flow channels or chambers of NMs. While the use of individual microelectrodes is relatively early and monofunctional, there have been impressive studies, such as the recording of communication between the hippocampus and cortical slices reported in 2010 [293]. Subsequent innovations and developments in MEA allowed the realisation of diverse functions of NMs.

Studies of MEA-equipped NMs have shown that selective chemical stimulation and multi-site neuronal signal recording provide groundbreaking solutions for neurobiology [292]; the compartmentalised structure facilitates optical stimulation and signal recording [294]; cultures of dorsal root ganglion neurons [295] and human induced pluripotent stem cell-derived neurons [296] demonstrate the feasibility of long-term non-invasive measurements; two-chamber [297] and four-chamber [275] structured NMs are suitable for the study of axonal conduction in neural networks; and the combination of multilayer photolithography, hydrogel, and MEA enables high-throughput studies of neural networks [298]. Notably, the combination of NMs with soft lithography and MEA allows for the creation of neuromuscular junction model to enable the recording of postsynaptic electrical activity, demonstrating the potential to be more sensitive than calcium imaging techniques [299]; and MEA-equipped NMs compatible with high-resolution video microscopy demonstrated the performance of simultaneously recording the intracellular dynamics and electrical activity in presynaptic axonal projections and postsynaptic neuronal targets [300].

Recently, the introduction of 3D nanostructures in MEA has improved the performance of extracellular monitoring and spatiotemporal minimally invasive supply of NMs. Oramany P. et al. demonstrated a cantilever MEA (over 200 μm) capable of producing curves and spikes [301]. David A. Soscia et al. reported a flexible 3D MEA that allowed the probe to stand upright without additional support, provided 256 channels, was biocompatible, easy to fabricate, and able to integrate with existing commercial electrophysiological hardware [302]. This is the first electrophysiological recording of human induced pluripotent stem cell-derived neurons in three dimensions simultaneously. In contrast, Peter D. Jones et al. achieved electrophysiological measurements in 3D neuronal cultures by means of complex NMs structures [298]. Giulia Bruno et al. fabricated hollow nano-antennas with high spatial resolution on electrodes, which enabled high-accuracy electrophysiological assessment of neuronal cells and simultaneous molecular delivery [292]. Moreover, probes with three-dimensional nanostructures are impressive tools for neural probing. This is because neural cell probes address the problem that conventional MEA does not have the integrated ability to stimulate surrounding neurons with real-time monitoring of neural networks [303, 304]. This solution enables precise measurement of synaptic latency in 3D neural networks [303], modulation of functional connectivity of neural regions in the brain [231], and stable continuous monitoring (more than one month) [232]. To alleviate the mechanical mismatch between rigid probes and cells, scholars have also developed microfluidic neural probes based on flexible/soft materials [206].

Semiconductor microcircuitry (SemiMC) is a special type of MEA that is also used to measure and record the electrical activity of neurons. While retaining the advantages of MEA, SemiMC has a higher spatio-temporal resolution, better downscaling capability, higher signal-to-noise ratio, and faster response speed than MEA because of its unique physical properties [227, 305, 306]. As early as 2005, SemiMC has been utilised to study electrical activity and postsynaptic excitation in single nerve cells [307]. Specifically, high-density MEA based on complementary metal-oxide semiconductors (CMOS) facilitates the monitoring of virtually all neurons in a circuit while enabling access to neurons at the subcellular, cellular, and network levels [308]. CMOS-based MEAs can also track information flows along axons [309], map synaptic connections [305], and monitor neuronal network activity [306]. Furthermore, NMs integrated metal-insulator-semiconductor field-effect transistors can be utilised to accurately guide neurons to form topological networks and simultaneously measure network activity [310].

Electrical stimulation of nerve cell (ESNC)

Electrical stimulation is an important biophysical level modulator and non-pharmacological intervention in clinical practice, with a significant ability to modulate cellular activity and promote tissue repair [311,312,313]. ESNC can activate neurons, while NMs can simultaneously record the electrophysiological responses of neurons, such as membrane potential changes, action potentials, etc., which allows for revealing the neuronal response mechanisms, precisely regulating and analysing neuronal activities, and establishing in vitro stroke models. Foremost, the macroscopic upper-limb motion can be directly applied to the microscopic nerve cells through the perception, decoding, and translation processes. Existing studies have demonstrated that electrical stimulation is effective in accelerating bone healing [312], interfering with brain tissue growth behaviour [313], and promoting nerve regeneration [314, 315]. Back in 2015, the National Institutes of Health was investing in research into the biophysical factors (including bioelectricity) that guide cellular behaviour, and there was interest from London-based GlaxoSmithKline in launching projects to restore health using electrical signals [316]. The focus of these grants and projects is the use of electricity to alter nerve activity.

MEAs equipped on NMs evoke action potentials along with non-invasive extracellular electrophysiological recordings and are one of the commonly used means of electrical stimulation today. In postsynaptic electrophysiological studies, it was demonstrated that activation of motor neurons through MEA can trigger muscle action potentials, demonstrating the potential for modelling human neuromuscular connectivity [299]. Moreover, Jian Du et al. prepared special NMs with perpendicular electric field based on polydimethylsiloxane and demonstrated that a cathode with a 20 Hz, 100 ms pulse at a potential gradient of 200 mV/mm enhanced neuronal differentiation [314]. Study by Jian Du et al. provides an effective and safe paradigm for ESNC and demonstrates the potential of ESNC to promote nerve repair and regeneration. Besides that, multiwell plate culture plates are also a solution with the same high-throughput properties of microfluidics [328]. Microfluidic structure study has shown that designing different microchannel widths can generate multiple electric field strengths in one device [319]. Similarly, the microfluidic resistor-ladder design coupled with a scalable channel layout can generate electric field strength intensities of three orders of magnitude from 2.1 mV/mm to 1.6 V/mm [320]. The latest study has allowed MEA-based time-coding electrical stimulation of neurons, and delayed electrophysiological responses have been observed experimentally [318]. Besides, in several reviews, multi-scale approaches to neuronal cell stimulation (including neuronal cell physiology, mathematical modelling of ion channels, and neuronal correlates) [329], biomedical applications of ESNC [328], electrically controlled cell behavior [330], and ESNC-enhanced cell regeneration [331,332,333] are systematically addressed.

Besides the above methods, there are emerging or interesting approaches to ESNC. Capacitive stimulators integrated into NMs can elicit action potentials for individual neurons [307]. Graphene has been shown to provide electrical properties suitable for ESNC and to be biocompatible [321]. Conductive nanofibres have enhanced electrical conductivity and satisfied the ESNC requirements in the experiments [322,323,324]. Specifically, conductive nanofibres prepared by combination of electrostatic spinning and aqueous polymerisation exhibit satisfactory biocompatibility while promoting more than 40% increase in the length of neuronal synapse growth upon the application of electrical stimulation [323]. Similarly, a dual-function (0.063 ± 0.029 S cm^− 1 electrical conductivity and 72%/12 h reduction in biodegradability) coaxial fibre consisting of an electrical shell layer and a soft-core layer was capable of enhancing neuronal cell activity through electrical stimulation [325]. And Lichun Lu demonstrated the superior biocompatibility [334] and ESNC properties [326] of nerve conduit prepared from polycaprolactone fumarate and polycaprolactone fumarate-carbon nanotubes (PCLF-CNT) in two studies, respectively, especially that electrical stimulation conducted by PCLF-CNT promotes both the extension of PC-12 neural synapses and intracellular connections [326]. In recent studies and reviews, electroactive hydrogels (e.g., graphene oxide hydrogels) encapsulated and electrically stimulated neuronal cells enhanced metabolic activity [335]; Soft conducting graphene scaffolds with electrical stimulation (± 800 mV/100 Hz for 1 h) increased the rate of neuronal differentiation by 5-fold, along with increased mature cell markers and electrophysiological features [327]; In a review of electrical signalling to regulate stem cell fate, Ying Kong et al. reported direct electrical stimulation, indirect electrical stimulation, and external field-responsive material-mediated electrical signalling (P. 22), in which advances in stem cell electrophysiology have been inspiring and informative for ESNC [336].

Perspectives and future directions

As highlighted in this review, spanning the subdomains related to stroke are essential for developing a comprehensive healthcare and rehabilitation system for upper-limb dyskinesia, integrating these subdomains into a cohesive whole. Consequently, global strategies are necessary to promote the application of knowledge and the translation of findings across these areas. This need is particularly urgent following the analyses by Luca De Iaco [337] and Helen Rodgers [3], who, in separate studies, concluded that single robot-assisted rehabilitation of the upper limb lacks clinical significance.

Closed-loop rehabilitation strategy for upper-limb dyskinesia after stroke

The closed-loop rehabilitation strategy proposed in this review represents a transformative approach to post-stroke upper-limb motion rehabilitation (P-UMR), integrating robotics, motion perception, metrics, neuronal microfluidics, and neuroelectronics into a cohesive framework. As illustrated in Fig. 1, this strategy leverages advancements across diverse fields, including medicine, biology, computer science, robotics, and bioelectronics, to address the limitations of current rehabilitation methods and promote neural remodeling. The core objective of P-UMR is to restore natural upper-limb motion by creating a dynamic, adaptive system that continuously monitors, analyzes, and optimizes rehabilitation interventions. Within the closed-loop strategy, upper-limb motion sensing analyzes and decodes this natural motion. The results are used as inputs to the rehabilitation robot, enabling the affected side to perform natural movements. Simultaneously, motion metrics quantify upper-limb motion and assess the affected side, providing a quantitative measure for evaluating rehabilitation outcomes. More importantly, these metrics can be transcoded to characterize electrical inputs for neuronal microfluidics, allowing for an exploration of the validity of current rehabilitation models. Neuronal microfluidics then establishes an in vitro stroke model and an electrical platform to investigate optimal rehabilitation modalities, such as visual mirroring [173, 338], robotic assistance [3, 59, 62,63,64, 66,67,68, 339], and complex rehabilitation [60, 61]. Ultimately, the findings are fed back into the rehabilitation process to enhance the restoration of natural motion in the affected limb.

The foundation of the closed-loop strategy lies in the precise analysis and decoding of natural upper-limb motion through advanced sensing technologies. Motion sensing systems, such as wearable sensors, IMUs, and sEMG, capture real-time kinematic and kinetic data, including joint angles, muscle activation patterns, and force generation. These data are processed using machine learning algorithms to decode the patient’s movement intent and assess the quality of motion execution. Motion metrics, derived from this analysis, provide quantitative measures of motor performance, such as range of motion, smoothness, and symmetry, which are critical for evaluating rehabilitation progress. The decoded motion data serve as inputs for rehabilitation robots, enabling them to assist the affected limb in performing natural, task-specific movements. For example, robotic exoskeletons or end-effector devices can use this information to provide adaptive support, adjusting assistance levels based on the patient’s real-time performance. This integration ensures that rehabilitation training is tailored to the individual’s capabilities and recovery stage, maximizing the effectiveness of each session.

NMs play a pivotal role in the closed-loop strategy by providing a platform to model stroke-induced neural damage and test rehabilitation interventions in vitro. NMs replicate the brain’s neural microenvironment, allowing researchers to study the effects of electrical stimulation, pharmacological agents, and other therapeutic modalities on neural repair and plasticity. Motion metrics, transcoded into electrical inputs, are used to characterize and optimize stimulation parameters within NMs, ensuring that the rehabilitation models are physiologically relevant and effective. For instance, NMs can simulate ischemic conditions and test how different patterns of electrical stimulation influence axonal regeneration, synaptic reorganization, and neural network recovery. These findings are then translated into clinical applications, guiding the design of personalized rehabilitation protocols. By bridging the gap between in vitro models and in vivo applications, NMs enable a deeper understanding of the neural mechanisms underlying stroke recovery and facilitate the development of targeted interventions. Neuroelectronics, including EEG and invasive neural interfaces, provide real-time feedback on brain activity during rehabilitation. This information is integrated with motion sensing data to create a comprehensive picture of the patient’s neural and motor function.

The closed-loop structure integrates motion sensing, robotics, neuronal microfluidics and neuroelectronics into a cohesive system. The interaction and interdependence among these subdomains are delineated below:

• Motion Sensing -- Robotics: Real-time motion data guide robotic devices to provide adaptive assistance, ensuring natural and task-specific movements.

• Motion Metrics -- Neuronal Microfluidics: Quantitative motion metrics are transcoded into electrical inputs for NMs, enabling the exploration of optimal rehabilitation modalities.

• Neuronal Microfluidics -- Electrical Stimulation: Insights from NMs inform the design of electrical stimulation protocols, enhancing their effectiveness in promoting neural repair.

• Neuroelectronics -- Motion Sensing: Neural activity data complement motion sensing, providing a holistic view of the patient’s progress and enabling more precise interventions.

The utilisation of brain imaging and nerve cells as the fundamental components of this interconnected framework serves to elucidate the underlying physiological basis. It is anticipated that this strategy will engender synergistic effects, thereby enhancing the overall effectiveness of rehabilitation.

Future directions

Envision a future where patients undergo rehabilitation through closed-loop, personalized, and lightweight strategies, enabling them to determine the optimal rehabilitation mode, customize individualized plans, and make timely adjustments throughout the recovery cycle, all while rehabilitating at home. This represents a comprehensive lifecycle rehabilitation process. Although we have not yet reached this stage, technological advancements are clearly steering us toward this promising future. However, numerous challenges persist, hindering the realization of this objective.

(1) How can we develop rapid, precise, and miniaturized diagnostic tools for real-time monitoring of stroke-affected brain regions?

• Integrate advanced neuroimaging techniques (e.g., portable fMRI or EEG) with machine learning algorithms to provide instant feedback on brain activity and structural changes.

• Establish standardized criteria for assessing biochemical and morphological changes in nerve cells post-stroke, enabling better evaluation of cellular damage and reparability.

The period following a stroke onset is characterized by a critical time constraint. Consequently, there exists a pressing demand for prompt, precise, and miniaturized diagnostic modalities for cerebral assessment, rooted in neuroimaging techniques. These initiatives offer instantaneous feedback on the evolution of stroke-affected regions, concurrently shedding light on the brain’s nuanced anatomical features, functionalities, and interconnections. This advancement is anticipated due to the pivotal role of prompt intervention post-stroke in dictating mortality rates, complication risks, and recuperative prospects. Furthermore, the alterations in the biochemical and morphological attributes of nerve cells subsequent to a stroke necessitate standardized and clearly delineated criteria. These criteria serve not only to gauge the extent of cellular injury (or reparability) but also crucially function as a fundamental gauge for evaluating neural microfluidics.

(2) How can ULrr systems be optimized for lightweight, modular, and open-source platforms to support diverse treatment protocols?

• Focus on reducing complexity by prioritizing anthropomorphic design, cost-effectiveness, and modularity.

• Develop open-source ULrr platforms that integrate multiple rehabilitation modes, such as motor-mirror, visual-mirror, and task-based therapies.

• Enhance internal and external feedback mechanisms by combining motion sensing technologies (e.g., depth cameras, IMUs) with real-time data analytics to adapt rehabilitation strategies dynamically.

ULrr is crucial for achieving lightweight rehabilitation across diverse medical scenarios. Therefore, reducing complexity should be prioritized, focusing on aspects such as anthropomorphism, economy, and modularity. Additionally, ULrr should evolve into an open-source modular platform to facilitate composite rehabilitation modes, including motor-mirror rehabilitation, visual-mirror rehabilitation, task-based rehabilitation, interactive rehabilitation, and multimodal integration. This diversity in ULrr can maximize personalized and participatory sports medicine while providing an ideal experimental platform for translational medicine research. Finally, internal and external feedback should be considered the “soul” of ULrr. Internal feedback connects the ULrr and the patient through motion sensing technologies, such as visual and physiological signals, which is essential for ULrr to emulate human-like functions. External feedback employs processes and technologies like motion sensing, metrics, and neural microfluidics to determine the optimal rehabilitation program, assess rehabilitation stages, and make timely adjustments to the rehabilitation strategy.

(3) How can we improve the accuracy and standardization of motion sensing technologies for clinical applications?

• Combine depth cameras with optical motion capture systems and machine learning to enhance motion data accuracy while maintaining ease of deployment.

• The integration of multi-camera/sensor fusion with large-scale models holds significant potential to enhance the accuracy of 3D motion reconstruction derived from 2D camera data.

• Develop model-based metrics that translate kinematics into standardized criteria based on upper-limb physiology, ensuring consistent and reliable rehabilitation assessments.

Depth camera-based motion sensing is particularly compatible with lightweight rehabilitation requirements, as it does not require patients to wear sensors and is easy to deploy and manage. However, improving the accuracy of captured motion data is a critical challenge. In contrast, optical motion capture systems are often regarded as the “gold standard” due to the synergistic complementarity of multiple lenses and infrared light sources [86]. Thus, synergistic integrating motion capture systems, depth cameras, and machine learning algorithms could enhance the accuracy of depth cameras by incorporating “gold standard” motion data. Similarly, high-precision lightweight motion sensing is essential for developing metrics, including rehabilitation assessments. Metrics act as a “ruler,” necessitating a unified quantification standard. Therefore, achieving high accuracy and consistent standards is imperative. Model-based metrics offer a potential solution, using a “white box” modeling approach that translates kinematics into criteria based on upper-limb physiology.

(4) How can neuronal microfluidics and neuroelectronic chips be advanced to better simulate stroke microenvironments and optimize rehabilitation modes?

• Design microfluidic devices that accurately replicate the biochemical and mechanical properties of stroke-affected neural tissues.

• Integrate microelectrodes and imaging techniques to monitor cell responses to restored electrophysiological signals, providing insights into neuroplasticity mechanisms.

• Explore the potential of neuroelectronic chips to bridge macroscopic motion data with microscopic neural activity, enabling a true “brain-on-a-chip” platform for rehabilitation research.

The era of the “Brain-on-a-chip” has arrived, heralding the transition to a true “Neuroelectronic chip.” Neuronal microfluidics, as the final component in a closed-loop rehabilitation system, serves as the interface between macroscopic upper-limb motion and microscopic nerve cells. In the macroscopic realm, sensing and metrics convert and restore upper-limb motion into characteristic signals or even electrophysiological signals from nerve cells to muscle activation. At the microscopic level, neural microfluidics simulate the stroke microenvironment and model cell damage physiology. Then accesses the restored electrophysiological signals in the macrocosm and monitors the cells through microcircuits and imaging techniques. And ultimately output changes in the cell state. This process inversely mirrors the normal physiological pathway in the human body, where electrophysiological signals originate in nerve cells, travel along neural pathways to muscles, drive bones, and result in macroscopic upper-limb motions. This neuroelectronic chip could be pivotal in exploring neuroplasticity mechanisms and optimizing rehabilitation modes, potentially ushering in a new era.

(5) How can interdisciplinary collaboration accelerate the translation of experimental findings into clinical applications?

• The publication of reviews or research articles addressing the theme of ‘closed-loop rehabilitation’ from diverse perspectives is encouraged to facilitate multidisciplinary and sub-disciplinary collaborative exchanges and advance the field.

• Conduct large-scale clinical trials to validate the efficacy of closed-loop rehabilitation strategies and ensure their scalability across diverse patient populations.

The future of stroke rehabilitation is poised for transformative advancements through the integration of cutting-edge technologies and interdisciplinary collaboration. By uniting expertise from neuroscience, bioengineering, robotics, and clinical medicine, researchers will develop closed-loop rehabilitation systems that dynamically adapt to patients’ needs in real time. These systems, which seamlessly integrate motion sensing, robotic assistance, neural microfluidics, and neuroelectronics, enable continuous monitoring and optimization of rehabilitation protocols. Such interdisciplinary efforts will not only enhance the precision and effectiveness of rehabilitation but also pave the way for scalable, home-based solutions that empower patients to take control of their recovery. Ultimately, this collaborative approach underscores the importance of breaking down silos between disciplines to create holistic, patient-centered care that maximizes functional recovery and improves quality of life.

Conclusion and discussion

This review proposes a new rehabilitation paradigm and future research directions, aiming to inspire researchers to explore the exciting possibilities arising from the convergence of subdomains. Specifically, it introduces the concept of “building a closed-loop upper-limb motion rehabilitation after stroke,” based on a synthesis of research related to the post-stroke brain, upper-limb rehabilitation robotics, motion sensing and metrics, as well as neural microfluidics and electronics. Under this new concept, lightweight upper-limb motion sensing enables collaborative rehabilitation robots to perform medical tasks across diverse scenarios. Motion metrics assess the rehabilitation stage to adjust strategies promptly and convert upper-limb motion into feature signals. Neuromicrofluidics accesses these signals, outputs cellular states, and provides personalized rehabilitation programs while evaluating effectiveness, assisted by research on the post-stroke brain. Thus, neuromicrofluidics will create a closed loop of guidance and feedback for rehabilitation.

Although this review primarily examines kinematic-based closed-loop rehabilitation strategies, it is imperative to acknowledge the significant complementary role of kinetics within this broader context. Kinetic parameters, including force and moment measurements, serve as critical indicators of muscle strength and motor control capabilities in stroke survivors. These quantitative metrics not only facilitate the comprehensive assessment of motor function but also inform the development of personalized rehabilitation protocols. Furthermore, kinetic analysis enables the evaluation of movement coordination and stability, thereby assisting in the identification of specific motor control deficits and guiding targeted therapeutic interventions. The implementation of real-time kinetic feedback mechanisms has demonstrated potential in enhancing patients’ motor awareness and engagement, consequently promoting more effective motor learning processes [144, 176, 177]. Therefore, more research in the future could explore the combination of kinetic monitoring and kinematic analysis to build a more comprehensive and precise closed-loop rehabilitation system to provide stroke survivors with more effective rehabilitation programs.

This field of research is still young, and significant efforts are required before it can be widely applied to motor rehabilitation after stroke. Historically, discoveries and advances in emerging fields need to integrate subdomains into a cohesive whole. Therefore, it is crucial to emphasize that global strategies and technological advancements facilitating the application of knowledge and translation of results across subdomains remain an open challenge. Nonetheless, this offers exciting promise of seamless integration across multiple disciplines and fields.