Better Understanding Rehabilitation of Motor Symptoms: Insights from the Use of Wearables

Yunus Celik; Conor Wall; Jason Moore; Alan Godfrey

doi:10.2147/POR.S396198

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Review

Better Understanding Rehabilitation of Motor Symptoms: Insights from the Use of Wearables

Authors Celik Y, Wall C, Moore J, Godfrey A

Received 23 May 2024

Accepted for publication 24 February 2025

Published 19 March 2025 Volume 2025:16 Pages 67—93

DOI https://doi.org/10.2147/POR.S396198

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor David Price

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Yunus Celik, Conor Wall, Jason Moore, Alan Godfrey

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

Correspondence: Alan Godfrey, Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK, Email [email protected]

Abstract: Movement disorders present a substantial challenge by adversely affecting daily routines and overall well-being through a diverse spectrum of motor symptoms. Traditionally, motor symptoms have been evaluated through manual observational methods and patient-reported outcomes. While those approaches are valuable, they are limited by their subjectivity. In contrast, wearable technologies (wearables) provide objective assessments while actively supporting rehabilitation through continuous tracking, real-time feedback, and personalized physical therapy-based interventions. The aim of this literature review is to examine current research on the use of wearables in the rehabilitation of motor symptoms, focusing on their features, applications, and impact on improving motor function. By exploring research protocols, metrics, and study findings, this review aims to provide a comprehensive overview of how wearables are being used to support and optimize rehabilitation outcomes. To achieve that aim, a systematic search of the literature was conducted. Findings reveal that gait disturbance and postural balance are the primary motor symptoms extensively studied with tremor and freezing of gait (FoG) also receiving attention. Wearable sensing ranges from bespoke inertial and/or electromyography to commercial units such as personal devices (ie, smartwatch). Interactive (virtual reality, VR and augmented reality, AR) and immersive technologies (headphones), along with wearable robotic systems (exoskeletons), have proven to be effective in improving motor skills. Auditory cueing (via smartwatches or headphones), aids gait training with rhythmic feedback, while visual cues (via VR and AR glasses) enhance balance exercises through real-time feedback. The development of treatment protocols that incorporate personalized cues via wearables could enhance adherence and engagement to potentially lead to long-term improvements. However, evidence on the sustained effectiveness of wearable-based interventions remains limited.

Keywords: wearable technology, rehabilitation, movement disorders, motor symptoms

Introduction

Movement disorders (eg, Parkinson’s disease, PD) pose significant challenges for millions of people worldwide, negatively impacting daily routines and overall well-being through a complex array of motor symptoms.^1–3 For example, gait disturbances such as shuffling make routine efforts to walk very challenging during habitual activities of daily living.^4,5 Equally, balance issues introduce a constant risk of falls even in familiar environments, diminishing a person’s confidence in mobility and fostering a dependency on assistive devices or the aid of others.^6,7

Typically, motor symptoms have been assessed via pen and paper approaches such the Unified Parkinson’s Disease Rating Scale (UPDRS),⁸ Expanded disability status scale (EDSS)⁹ or the Berg Balance Scale.¹⁰ Those methods, while valuable, come with subjective limitations. Additionally, use of those pen and paper approaches provide only a snapshot in time and do not capture the dynamic changes in symptoms that can occur over an extended period.⁸ The limitations of traditional assessment scales highlight the critical need for objective, quantitative data.

Routine use of standalone (uni-modal) wearable technologies such as bespoke inertial measurement units (IMUs ie, accelerometers,^11,12 or gyroscopes^13,14), force sensors,^15,16 pressure sensors^17,18 and electromyography (EMG)^19,20 could effectively address current assessment limitations by providing objective, continuous, real-time monitoring of motor symptoms. However, the integration/fusion of multiple sensing modalities (multi-modal) could provide holistic data on motor symptoms.^21,22 Wearables have shown utility in the early detection^23–26 of motor symptoms and in tracking symptoms^27–29 over time. Furthermore, wearables could deliver personalized rehabilitation programs via virtual reality (VR),^30,31 augmented reality (AR),^32,33 headphones,^34,35 or wearable exoskeletons^36,37 tailored to the specific needs of each individual. Personalization could be made optimal by using artificial intelligence (AI) to analyze large volumes of data to identify patterns, predict outcomes, and/or adapt/tweak interventions. For example, VR and AI based rehabilitation systems have been used to adapt task difficulty and feedback based on real-time motion data, ensuring a therapy remains challenging yet achievable.³⁸ That personalized approach is important to ensure rehabilitation programs are not only effective but also efficient, reducing the time required to achieve meaningful recovery milestones.

To date, numerous reviews have examined wearables for monitoring motor symptoms.^39–50 Yet, relatively few studies have investigated the application of wearables in the rehabilitation of motor symptoms.^51–54 those that have are often limited in scope, focusing exclusively on specific sensor types, such as inertial sensors alone, or on a particular neurological condition, such as PD.⁵² Equally, reviews have been constrained to specific intervention modalities eg, telerehabilitation,⁵¹ rhythmic or dance-based interventions,^55–57 VR and AR,^58,59 or robotic and exoskeleton-assisted therapies.^36,60,61 This highlights a gap in the literature regarding the broader potential of wearables in diverse rehabilitation contexts and across a wider range of neurological conditions.

The aim of this review is to provide a comprehensive overview of how current research leverages wearables in the rehabilitation of motor symptoms. We aim to highlight the existing gaps in wearables, interventions, and neurological conditions observed in the literature. This review takes a broad perspective, setting itself apart from earlier studies by covering various wearables across different cohorts and multiple training and rehabilitation programs. The structure of the review is as follows: it begins with a background of common motor symptoms associated with neurological conditions, as a basis for later sections. Next, a systematic search is presented using terms derived from previous reviews and results are presented, highlighting trends and statistics in the literature and an assessment of how various wearables are applied in rehabilitation studies. The review concludes with a discussion on the diverse applications of wearables in rehabilitation, their effectiveness, and the role of personalization, while addressing current limitations and offering insights into potential advancements and future improvements in the field.

Background: Motor Symptoms

Movement disorders encompass a wide spectrum of neurological conditions characterized by abnormal or impaired movement patterns, each posing distinctive challenges and complexities in their diagnosis and management. This section provides a brief introduction to gait disturbances, freezing of gait (FoG), tremors, and balance, outlining their key characteristics and fundamental descriptions.

Gait Disturbances

Gait disturbances refer to abnormalities in walking that can result from a wide range of conditions affecting the nervous system, musculoskeletal system, or both.^62–64 In the context of neurological conditions, gait disturbances often reflect underlying damage to or dysfunction in the areas of the brain, spinal cord, or peripheral nerves that are involved in movement control.^62,65–67 Shuffling gait is a type of gait disturbance characterized by short, dragging steps, often associated with reduced foot clearance and difficulty initiating movement.⁶⁸ This gait pattern is most readily associated with PD.^64,69

Hemiplegic gait is a distinctive pattern of walking often observed in individuals who have experienced significant muscle weakness or paralysis on one side of their body, commonly due to stroke.^70,71 Hemiplegic gait is characterized by a circumduction movement, where the affected leg is swung outward and forward in a semicircle to compensate for the reduced control and strength.⁷² Previous studies show a decrease in walking speed and an increase in the energy required for walking with hemiplegic gait, alongside an altered gait asymmetry due to the imbalance between the affected and unaffected sides.^73–75 The range of motion in the hip, knee, and ankle joints on the affected side is typically restricted, with a notable decrease in the ability to achieve full joint extension during the walking cycle.^73,76 These biomechanical changes are further compounded by modifications in the arm swing on the affected side, which can affect overall balance and gait stability.⁷⁷

Ataxic gait is characterized by a lack of voluntary coordination of muscle movements, resulting in a wide-based, unsteady, and irregular gait.⁷⁸ Individuals exhibiting an ataxic gait often demonstrate a marked variation in stride length and an inability to maintain a straight trajectory, with a tendency to veer unpredictably.⁷⁹ This gait irregularity is further compounded by an impaired sense of balance and spatial positioning, which significantly increases the effort required to walk and the risk of falls.⁸⁰ Conditions such as cerebellar ataxia (CA), multiple sclerosis (MS) have been closely associated with this gait pattern.^81,82

Freezing of Gait (FoG)

Freezing and festination of gait are often recognized as characteristic features associated with akinesia. FoG is a neurological phenomenon characterized by sudden and temporary episodes of immobility during walking. It can be described as a sudden and involuntary inability to initiate or continue walking and it typically lasts for a few seconds to minutes.⁸³ High-frequency oscillations and festinating steps, observed in the pre-FoG and during FoG phases, have been established as pivotal markers of this phenomenon.⁸⁴ FoG is closely associated PD and is extensively studied,⁸⁵ but also occurs frequently in other conditions like progressive supranuclear palsy (PSP).⁸⁶

Tremor

Tremor is defined as a phenomenon characterized by oscillating and rhythmic involuntary movements occurring in relation to a fixed point, axis, or plane.⁸⁷ Tremors are classified into five distinct categories (rest, postural, kinetic, isometric, and action^88–92) which are based on when the tremor occurs during voluntary muscle activation, maintenance of a stable posture, or active movement.⁹³

Natural resonant frequency is an important concept to understand tremor. Symptomatic resting tremors usually have a frequency between 4 and 5 hertz (Hz) whereas postural tremors with dominant peaks around 6 Hz.⁹⁴ Tremor can present as either an isolated symptom of a disease, as seen in essential tremor (ET), or as a component of various neurological disorders, including PD,^95,96 stroke,^97,98 traumatic brain injury (TBI),⁹⁹ multiple sclerosis (MS).¹⁰⁰

Balance

Poor postural balance/control is characterized by difficulties in controlling body alignment and stability during various activities eg, standing, walking, or sitting.¹⁰¹ When individuals experience poor postural balance, they may sway, stumble, or fall more frequently than those with normal balance control. Poor postural balance can be commonly seen in various neurological conditions such as PD due to a loss of dopamine-producing neurons in the brain, which affects motor control, MS as it affects the central nervous system and stroke which results in damage to areas of the brain responsible for balance and coordination.^101–104 Postural instability is typically diagnosed subjectively through a clinical evaluation that involves physical examination, relevant laboratory tests, imaging, and an assessment of the patient’s gait pattern.¹⁰⁵ However, there are also more objective methods available, such as the measurement of trunk velocity changes in response to physical perturbations, which can serve as potential indicators of gait stability¹⁰⁶ and more.^23,50

Methods

Search Strategy and Study Selection Process

To identify relevant articles, a search was executed across two major scientific databases: PubMed and ScienceDirect. This review targeted journal articles written in English that explored the application of wearables in rehabilitation of movement disorders. The search strategy used a structured, stratified approach with specific search terms, Table 1. Specifically, wearables were categorized based on the types of data they generate:

Group 1: Inertial-based devices such as bespoke inertial measurement units (IMUs: accelerometers, gyroscopes) and commercial devices like smartwatches and smartphones as well as research grade technologies such as Actigraph^™, Kinesia^™ and Parkinson’s KinetiGraph (PKG^™).
Group 2: Pressure and force measurement devices: Pressure sensors, insoles and foot switches.
Group 3: Electromyography (EMG).
Group 4: Interactive and immersive technologies: VR, AR, headphones and gaming consoles.
Group 5: Wearable assistive robotic systems: Robots, exoskeletons and vibrotactile devices.
Group 6: Multimodal sensing.

Table 1 Search Terms

The review encompassed articles published from 01 January 2000 until 01 March 2024. Following the search, the process of article selection was guided by the PRISMA guidelines¹⁰⁷ (Figure 1) and involved: (1) YC and AG independently screened titles from the merged database results after duplicates were removed to identify relevant articles; (2) they then examined the titles and abstracts of these articles, resorting to full-text reviews when necessary to determine if the studies met the review criteria; and (3) YC, CW, JM and AG reviewed full texts to decide on their inclusion (Table 2). Additionally, the reference lists of all studies included in the review were thoroughly examined to identify any additional relevant publications that could be added. Throughout the selection process, decisions to include or exclude studies were collaboratively made by all authors.

Table 2 Eligibility Criteria

Data Extraction

Data were synthesised into a table format by one author (YC) and another (AG) confirmed data entry. For each article, data were extracted on several key aspects, including the participants involved, the wearable used, the study protocol, any reference or additional measures employed, the outcome measures assessed, and the findings.

Search Results

The database search identified 341 articles, and an additional 17 articles were included through a citation search. Following the removal of duplicate records, reviews, books and book chapters, a total of 247 articles assessed for eligibility based on predetermined inclusion and exclusion criteria. Overall, 116 articles met the inclusion criteria (see Supplementary Material, search results). The full flow diagram of the screening process including the number of studies identified and excluded is shown in Figure 1.

Figure 1 The article selection process flow diagram.

Gait disturbance (52 articles) and postural balance (55 articles) are the most frequently studied, followed by FoG (8 articles) and tremor (5 articles). Some articles examined multiple motor symptoms within a single study. A total of 20 articles utilized inertial sensors to capture movement. Additionally, 3 articles focused on the application of pressure and force measurement devices to assess physical interactions and loads. EMG was employed in 8 articles. Furthermore, interactive and immersive technologies (VR, AR and headphones) were explored in 57 articles. Lastly, 32 articles reported on the use of wearable assistive robotic systems, Figure 2.

Figure 2 Wearable types used in reference rehabilitation articles.

For the rehabilitation of motor symptoms, wearables were effectively used both as therapeutic tools within rehabilitation programs and as monitoring devices to assess progress. Table 3 presents a categorization of research articles based on wearables, motor symptoms, and neurological cohorts.

Table 3 Categorization of Research Articles That Focus on Different Motor Symptoms Associated with Specific Neurological Disorders and Wearable Technology

Stroke survivors (SS, 44 articles) and people with PD (38 articles) were the most extensively researched groups within the neurological population followed by MS with 13 articles, cerebral palsy (CP) with 12 articles, TBI with 5 articles, ET with 3 articles, and spinal cord injury (SCI) and PSP, each represented by one article. Rehabilitation of gait disturbances has been investigated in SS with 18 articles, PD with 16 articles, MS with 6 articles, CP with 4 articles, TBI with 1 article, and SCI with 1 article. FoG has been exclusively studied in people with PD, with 8 articles. Balance recovery has been examined in SS (25 articles), PD (12 articles), CP (8 articles), MS (7 articles), TBI (4 articles), and PSP (1 article). In tremor treatment, ET is the most studied cohort, with 3 articles, followed by PD with 2 articles and SS with 1 article. Overall, PD and SS are the most frequently studied cohorts across various motor symptoms, particularly gait disturbance and balance recovery.

Wearables in Rehabilitation of Motor Symptoms

Table 4 presents all studies, providing details on intervention type, study protocol, number of subjects, clinical tests, type and quantity of wearables, placement of wearables, features targeted, and study findings.

Table 4 All Included Studies (Inc. randomized Controlled Trials And Clinical Trials) That Used Wearable During Rehabilitation of Movement Disorders

Group 1

Gait Disturbances and FoG

Inertial sensors can play a role in gait rehabilitation by providing real-time feedback on spatial and temporal gait parameters, enabling patients to alter movements and improve functional performance in real-life and home-based settings.¹¹⁸ For instance, the Gamepad system used IMUs to support the delivery of immediate auditory and visual cues, facilitating task-oriented training that mimicked daily activities in people with PD.¹⁰⁹ That approach enhanced motor learning and facilitated fine-tuning of the system during exercises. Similarly, CuPiD integrated IMUs with a smartphone application to provide real-time feedback on gait parameters such as cadence and stride length, with a particular focus on home-based rehabilitation for PD.¹⁰⁸ Despite their potential, the effectiveness of inertial sensors can vary across conditions and applications. For example, one study used these sensors to assess gait changes in a MS cohort over 18 months. While parameters like stride velocity distinguished mildly and moderately disabled participants from healthy controls, no gait decline was observed over time. This highlight the sensors’ ability to detect impairments but raised questions about sensitivity in tracking disease progression.¹¹² Wearables have also been explored to address FoG through external sensory cues. A closed-loop system with inertial sensors detected the stance phase of gait and delivered phase-dependent vibrations to the wrist. That provided real-time proprioceptive feedback to enhance sensory integration and motor coordination.²⁴¹

Tremor

Inertial sensors can provide real-time feedback on tremor characteristics, such as amplitude and frequency. One study showed that IMUs could accurately assess kinetic tremor severity during wrist movements by measuring angular displacement and velocity to enable clinicians customize a rehabilitation protocol.¹¹⁶ Similarly, IMUs have been used to monitor tremor dynamics during tasks like wrist flexion and extension under varying loading conditions. That approach distinguished central tremor components from mechanical reflex contributions, providing deeper insights into tremor mechanisms to help guide the development of more targeted therapeutic interventions.¹¹⁷ Additionally, IMUs have proven useful in injection-based therapies. For instance, these sensors guided botulinum toxin type A (BoNT-A) injections in PD and ET patients. Specifically, IMUs improved treatment precision by identifying target muscles and assessing tremor severity through amplitude and frequency analysis.¹¹⁵

Balance

Application of inertial sensors can be important to improve balance, as they can provide real-time feedback on postural stability and sway dynamics.^180,197,242 Inertial sensors measure parameters such as centre of pressure (CoP) trajectory and sway velocity, enabling patients to make immediate postural adjustments during exercises.¹²¹ For example, the RIABLO system combined IMUs with biofeedback, enabling users to visually monitor their balance performance and receive auditory cues for task-specific training that mimicked daily activities.¹²⁴ Similarly for SS, a home-based program combined a balance disc with a smartphone inclinometer app to deliver real-time feedback during seated balance exercises. Over four weeks, SS demonstrated significant improvements in postural control and daily living activities compared to conventional therapy.¹¹¹

Group 2

Gait Disturbances and FoG

Foot pressure sensors and insoles provide a portable and discrete approach for objective data related to pressure distribution and some temporal gait parameters.¹⁵ Rhythmic auditory stimulation (RAS) combined with foot pressure sensing has been used to analyze gait phases (eg, loading response, flat-foot, pre-swing, swing) by detecting events like heel strike and toe-off. RAS at 110% of preferred cadence significantly improved gait phase distribution, reducing double support time and increasing single support time to enhance gait stability in PD.^243–²⁴⁵ However, its long-term effects remain unclear.¹²⁶

A pilot study on gait training used footswitch-equipped insoles to measure stride time variability and gait speed during single and dual-task conditions (eg, verbal fluency, arithmetic tasks). Over 12 sessions in four weeks, participants improved gait speed and stride time variability, with gains transferring to untrained dual-tasks, suggesting cognitive-motor benefits but the small sample size limited the generalizability of the results.¹²⁷ Another study used silicone insoles with thickened pads to apply controlled plantar pressure, improving sensory feedback through pressure sensors. That method enhanced spatio-temporal gait parameters and reduced FoG episodes in PD.¹²⁸

Group 3

Gait Disturbances and Balance

EMG is widely used to assess muscle activation patterns and neuromuscular coordination during functional tasks.^133,246 Integrating EMG with other sensing modalities like IMUs enables real-time monitoring and feedback by simultaneously capturing muscle activity and movement patterns, enabling precise evaluation of interventions such as robotic exoskeleton training¹³³ and treadmill-based rehabilitation.²⁴⁶ In CP, EMG alone has been essential for assessing the impact of selective percutaneous myofascial lengthening. It has been used to show improvements in gait function and strength in key lower-limb muscles.¹³⁰ Similarly, EMG-triggered functional electrical stimulation and biofeedback systems showed an improvement of voluntary muscle activation and gait symmetry, particularly in SS.¹³² EMG has also been used to evaluate neuromuscular adaptations during progressive resistance training. It reliably measured dynamic and isokinetic knee muscle strength and assessed its impact on gait performance in SS.¹³¹ In balance rehabilitation, task-oriented EMG biofeedback has proven effective in enhancing muscle strength and motor relearning. For example, targeting the tibialis anterior has improved anterior-posterior balance by promoting real-time feedback and motor learning principles in SS.²⁴⁷

Group 4

Gait Disturbances

Interactive and immersive technologies provide dynamic and customizable environments for patient engagement, precise tracking for assessment, and innovative therapeutic exercises.^31,144 A study used a closed-loop AR device with accelerometer-driven cues to improve walking speed, stride length, and cadence through adaptive visual feedback. Post-training, 70% of participants maintained at least a 20% improvement in speed or stride length.¹⁴⁵ Another study used Google Glass with an AR dance app to deliver cues for improving mobility in people with PD. Standard assessments showed enhanced mobility under cognitive load following the intervention.³² A similar study used a portable auditory cueing device integrated with smart glasses, a smartphone app, and gait analysis to improve walking in people with PD. Listenmee^® auditory cues increased walking speed by 38.1%, cadence by 28.1%, and stride length by 44.5%.¹⁴⁶

FoG

VR has been used for dual motor-cognitive training in those with FOG by creating immersive environments that require users to perform cognitive and motor tasks simultaneously. That approach aims to mimic real-world complexities, improving dual-task performance and enhancing functional outcomes.¹⁵⁶ AR platforms, such as Google Glass^™, have been investigated in pilot studies to deliver real-time, context-aware visual cues, showing preliminary success in reducing the incidence of FoG.¹⁵⁴ Additionally, a combination of VR and physical practice using video self-modelling has proven feasible and acceptable for rehabilitation to helps patients visualize and replicate optimal gait patterns to improve walking.¹⁵³

Although it is evident that immersive technology supports rehabilitation, its effectiveness can vary among individuals where comparative research shows that treadmill training with VR affects patients with and without FoG differently.¹⁵⁵ Regardless, virtual environments offer a powerful tool for replicating FoG triggers, enabling controlled studies and targeted interventions while providing valuable insights into motor initiation and inhibition, thereby deepening the understanding of FoG mechanisms.^248,249 For instance, complex tasks like turning, a common FoG trigger, can be addressed using AR visual cues to improve gait control.²⁵⁰ Similarly, VR-based interventions for overground walking demonstrate that virtual improvements can translate effectively to real-world ambulation, enhancing therapeutic outcomes.³¹ AR-enhanced smart glasses further integrate augmented visual cues into daily life, helping to reduce FoG episodes in real-world settings.²⁵¹ Innovations like the “Crossing Virtual Doors” VR paradigm simulate specific gait challenges, advancing research on spatial navigation difficulties associated with FoG.²⁵² Additionally, wearable AR applications utilizing holographic cues have shown promise in improving walking and reducing FoG episodes, offering a practical and portable solution for patients.²⁵³

Balance

VR-based balance exercises provide immersive environments that can help improve balance outcomes.¹⁸⁷ Dual-task VR training has shown significant benefits for postural balance in chronic SS by integrating cognitive challenges with motor recovery.¹⁴⁷ Telerehabilitation programs using VR video games enhance balance in people with MS, showcasing remote, technology-driven care.¹⁶³ For adolescents with CP, tailored VR programs offer interactive solutions to improve functional balance and mobility.¹³⁷ Portable VR balance devices are also advancing mild traumatic brain injury (mTBI) care by enabling assessment, continuous monitoring, and therapy.²⁵⁴ Combining VR with auditory biofeedback has shown improvements in balance-related sensory impairments for mTBI patients.¹⁸⁶ Additionally, autonomous VR systems have demonstrated safety, usability, and compliance which highlight the potential for patient-centred, home-based balance training in SS.¹⁴⁸ Nevertheless, it is reported that challenges remain in translating virtual balance improvements to real-world postural control, particularly for chronic SS.¹⁷⁰

Group 5

Gait Disturbances

Wearable assistive technologies are favoured for their seamless integration into daily life, real-time feedback, and continuous monitoring of real-world activities.^200,239 For individuals with SCI, powered lower-limb exoskeletons enable assisted walking, promote gait retraining, and improve overall functional independence.¹⁹³ Similarly, in SS, the Hybrid Assistive Limb^® (HAL), combined with neuro-controlled robotics, demonstrated significant improvements in gait parameters after structured training programs²⁰⁰ Another exoskeleton, the stride management assist system (SMA^®), refined spatiotemporal gait characteristics in SS by delivering precise, real-time gait adjustments.¹⁹⁹ Beyond SS, wearable adaptive resistance training improved ankle strength and walking efficiency in individuals with CP by providing adjustable, personalized resistance.¹⁹⁰ Randomized trials further highlighted the superior adaptability and precision of robotic systems like SMA^® compared to traditional gait training.²⁰²

FoG

Assistive robotic systems, such as robot-assisted treadmill training, show promise in managing FoG symptoms in people with PD. For instance, a pilot study demonstrated that repetitive robot-assisted treadmill training reduced the occurrence.²⁰⁹ Moreover, the sustained benefits of such technology have been observed in a study focusing on the long-term effects of robot-assisted treadmill walking. Over extended use, this modality has demonstrated a capacity to reduce the severity and frequency of FoG in people with PD.²⁰⁷ Expanding the scope of intervention, an overground robot-assisted gait trainer has been evaluated for its efficacy in treating drug-resistant FoG in PD. This innovative system allows for more naturalistic walking scenarios, which can be particularly beneficial for patients who experience FoG in real-world environments.²⁰⁸

Balance

The domain of balance rehabilitation has been greatly enriched by the introduction of assistive robotic systems, which have proven to be an asset across a spectrum of neurodegenerative conditions. In a previous work, tongue electro-tactile biofeedback used the tongue’s sensitivity to deliver real-time posture correction signals to advance balance rehabilitation therapy.²¹⁰ The use of vibro-tactile biofeedback for trunk sway is another novel approach that has shown characteristics of improvement in balance control among people with MS. By delivering sensory cues about body sway, this method helps patients adjust their posture to enhancing stability and reduce the risk of falls.²¹¹

High-intensity robot-assisted gait training was evaluated for its impact on dynamic balance and aerobic capacity in SS, and benefits for both mobility and cardiovascular health were reported.²¹⁶ Evidence from robot-assisted axial rotations provides insights into the early balance impairments in PD, suggesting that robotic systems can detect and potentially remediate balance issues before they become clinically apparent.²⁵⁵ A study on hemiparetic SS compared robotic balance training (BEAR) with intensive balance training and conventional rehabilitation. The BEAR group, utilizing robotic technology, demonstrated significant improvements in balance assessed by Mini-BESTest scores.²¹⁷

Group 6

Gait Disturbances

Feedback mechanisms (positive and corrective feedback, interactive rhythmic cues) are pivotal in providing real-time insights and adjustments to gait patterns, contributing to notable improvements stability and overall mobility.^{35,169,213,256} A significant amount of research supports the effectiveness of these methods. Examples include a study that combined IMU and Google Glass to deliver visual and auditory cues for gait assistance, using flashing lights, optic flow, and metronome sounds. Results showed a clinical preference for auditory over visual cues.²³⁰ Another study investigated the impact of walking to music and metronomes on MS, using IMUs and headphones to explore auditory-motor coupling. With IMUs on the ankles measuring cadence and step time, findings highlighted the effectiveness of music in enhancing gait characteristics.¹¹³ A study combined IMUs and video-based wearable glasses to enhance fall risk assessment, with IMUs capturing gait data and glasses providing environmental context. Integrating both technologies offered a more comprehensive evaluation.²⁵⁷

Previous research has explored use of exoskeletons and wearables to enhance gait retraining and monitor improvements. In people with PD, overground gait training with a wearable Active Pelvis Orthosis (APO) exoskeleton and IMUs were evaluated. The APO adjusted gait in real time, while IMUs tracked dynamics. Training improved hip motion, gait speed, and stride, with effects lasting one month, though gait variability normalized only immediately post-training.³⁶ In a different study on the Keeogo^™ exoskeleton for MS patients, researchers used a powered exoskeleton, IMUs, and an Actigraph^™ to assess its effects. While gait performance slightly declined when wearing the device, unassisted performance significantly improved after two weeks of home use.¹¹⁴ Additionally, the “WalkMate” system, incorporating pressure sensors and headphones, was used to deliver interactive rhythmic cues for gait retraining in people with PD. Those cues gradually but effectively reduced gait fluctuations.²²⁸

Balance

A telerehabilitation study used smartphone-based IMUs and exergames for balance training in early subacute SS. IMUs tracked movements, and exergames provided feedback, leading to improved balance and functional independence compared to conventional treatment.²³⁷ Elsewhere, researchers used foot-mounted IMUs and headphones to study auditory input effects on gait stability in people with PD, with and without FoG. Those with FoG showed the most stable gait with continuous cueing, while non-FoG individuals showed no significant differences across conditions.²³¹ Alternatively, a vibrotactile biofeedback device with used with a Nintendo Wii Balance Board for balance training in chronic SS. The device provided vibration cues to improve postural control, while the Wii Board tracked CoP patterns, resulting in reduced postural variability and improved clinical balance performance.²²⁰

Discussion

The search findings reveal that wearables are playing a growing role in motor rehabilitation, with gait disturbances and balance recovery being the most studied areas. Interactive technologies, (VR and AR), were the most frequently used, particularly for gait and balance recovery. Wearable assistive robotic systems were the most favoured technology for tremor treatment. PD and SS were the most studied cohorts, while conditions like MS and TBI received less attention. All key findings from the literature search are presented in Box 1.

Box 1 Key findings

Effectiveness

Studies such as those focusing on balance exercises,²²² and gait training^127,225 highlight the effectiveness of wearables in delivering targeted and data-driven rehabilitation. Those approaches have demonstrated clear benefits in improving the quality of life for individuals with movement disorders.^34,110 For instance, notable improvements in gait parameters²²¹ and enhanced motor performance in high-intensity gait training²²⁵ compared to other methods reveal the potential of personalized, real-time monitored interventions to address specific deficits in PD. However, this is not always effective. A previous work that utilised Gamepad system led to significant improvements in balance but showed no progress in gait outcomes.¹⁰⁹ This contrast suggests that while physical training can lead to progress, some complex and highly coordinated movements may not improve. Furthermore, the persistence of any longitudinal improvements is not well documented.^36,217 This indicates that future studies should need for follow-up assessments to confirm long-term outcomes.

Music and Rhythm Therapy

The diversity in intervention designs and wearable applications underscores the complexity of effectively deploying these technologies across neurological conditions. However, many PD-based studies demonstrate how wearables can enable precise and targeted rehabilitation. For instance, interventions such as music-based gait training^{35,110,126,228} and cueing/feedback training²³¹ leverage wearables to manage and enhance motor performance in that cohort. Those technologies facilitate real-time tracking of gait parameters, such as speed, stride length, and variability, while enabling rhythmic auditory feedback, which has been shown to improve motor symmetry, coordination, and arm swing range of motion.³⁵ Wearables play a key role in delivering rhythmic auditory cues, such as music or metronomes, with music-based cues often preferred for their engaging nature, which promotes adherence to therapy.²³² That approach does extends beyond PD, as demonstrated in SS¹¹³ and people with MS,¹¹³ where music-based gait training reduced movement execution duration, improved movement precision, and supported task-oriented motor training.

Additionally, the use of time-stretching technology in wearables enables personalized auditory cueing by adjusting music tempo to match individual motor capabilities without altering pitch.¹¹⁰ Studies comparing rhythmic auditory and visual cueing²³⁰ further reinforce the effectiveness of auditory cues, as they tend to produce greater improvements. These advancements highlight the versatility of wearables in integrating real-time feedback and personalized interventions to address motor impairments across a range of neurological disorders.

Virtual and Remote Rehabilitation

Studies focusing on rehabilitation using VR-AR collectively highlight the nuanced effectiveness of such technologies in enhancing motor function, balance, and overall physical activity, albeit with varying degrees of success and application specificity.¹⁵⁵ Biofeedback training¹⁰⁹ and VR-based interventions^172,183,235 have shown significant improvements in balance and motor function, emphasizing the potential of real-time feedback and immersive environments to augment traditional rehabilitation. Particularly, VR-based telerehabilitation for SS¹⁷² mirrored the efficacy of in-clinic interventions. The application of AR and exergames presents an innovative approach to address dual-task declines associated with PD while enhancing balance, albeit with mixed outcomes regarding the significance of improvements in motor outcome measures.^32,236,237 This suggests a potential area for further exploration, particularly in understanding the contexts in which AR and exergames yield the most benefit. Interestingly, the efficacy of interventions often correlated with the specificity of the technology to the rehabilitation goal, as seen in the smartphone-delivered automated feedback training¹⁰⁸ which was both feasible and effective for promoting gait training in PD.

Conversely, telerehabilitation (remote) interventions,^233,237,238 have expanded the accessibility of rehabilitation services. These technologies contribute to accessibility by reducing the need for in-person visits, enabling people to receive therapy from the comfort of their homes, which is particularly beneficial to those in remote or underserved areas. Moreover, they offer a cost-effective alternative to traditional rehabilitation by minimizing travel expenses, reducing clinic overheads, and enabling scalable delivery of personalized care, ultimately making rehabilitation more inclusive and sustainable for a broader population.²⁵⁸

Exoskeletons for Rehabilitation

Exoskeletons have shown varied efficacy in neurological rehabilitation, with improvements reported in gait parameters, balance, and mobility across conditions like stroke, CP, PD, MS, and SCI.^{199–203,239} However, while studies in stroke highlight enhanced brain activation and functional mobility, the reliance on exoskeletons for restoring gait function raises questions about the sustainability of these gains without continued use. For CP, the results suggest non-invasive alternatives to invasive procedures, yet the long-term impact on motor function remains underexplored. In PD, improvements in range of motion and stride length are promising,^36,192 but evidence of durable outcomes beyond short-term interventions is limited. Despite advancements in unassisted mobility for SCI and MS,^114,193 the high cost, accessibility, and adaptability of exoskeletons pose significant barriers to widespread adoption. These challenges highlight the need for a thorough evaluation of their long-term effectiveness and practicality in everyday settings.

Increasing Adherence: Personalisation

The concept of personalizing content within wearables, especially through VR environments and music selections, offers a promising avenue to enhance user engagement and adherence, particularly.²⁵⁹ This strategy not only leverages the intrinsic motivation and emotional engagement elicited by personalized experiences²²⁷ but also extends to extrinsic factors, where intervention methods are tailored to fit the unique physiological conditions of the individual.^260,261 For example, personalization in gait retraining may include the use of biofeedback techniques, which adjust critical aspects of the patient’s walking pattern, such as cadence or gait speed and provide real-time data that allows patients to make immediate adjustments.²⁶²

Music-based interventions that cater to individual musical preferences have been shown to improve gait and mobility in people with PD.³⁵ Furthermore, personalized VR environments that reflect users’ interests or past experiences can potentially increase adherence to rehabilitation protocols by creating a more immersive and enjoyable therapeutic experiences. While direct evidence is limited, the principle of personalization increasing adherence is supported by broader research in digital health interventions.²⁶³ Nevertheless, personalization poses challenges, such as variability in preferences and the need for extensive content libraries, increasing cost and complexity. Additionally, users with cognitive impairments or limited tech skills may find personalized options overwhelming.

The limitations of personalization could be addressed through AI by utilizing data from sensors, user feedback, and performance metrics to develop adaptive and tailored rehabilitation plans, ensuring effectiveness and usability.²⁶³ AI-driven VR and AR systems can modify therapeutic tasks to align with an individual’s pace and capabilities. One approach involves dynamically adjusting task difficulty and providing real-time feedback through a smartphone-based VR app so that therapists can customize cognitive and social rehabilitation programs to match the specific need of each patient.²⁶⁴ Similarly, AI-driven VR systems can use advanced motion-tracking technology to monitor a user’s three-dimensional movement, allowing them to evaluate the quality of exercises and support adherence to personalized rehabilitation programs.²⁶⁵ Additionally, wearable data, combined with AI, can classify body movements with high accuracy and this could enable therapists to track progress and adjust interventions in real-time.²⁶⁶

Limitations of Current Literature

Protocols vary significantly across studies, with intervention durations ranging from a single session^35,239 to programs spanning several months.²²⁷ This variability makes direct comparisons challenging and may affect the sustainability of the intervention’s benefits. Short-term interventions might not capture long-term outcomes, whereas longer interventions may better reflect sustained effects but are more challenging to standardize and control. In terms of methodological robustness, most studies adopted a randomized controlled trial format. However, some limitations are present, such as the relatively small sample sizes in certain studies²⁴⁰ (eg, with 7 CP patients) and the absence of long-term follow-up data. That underscores the need for larger-scale studies and extended monitoring to fully comprehend the long-term implications of wearables in rehabilitation. Moreover, the majority of studies did not consider age matching during recruitment, which could introduce bias, especially when interventions target conditions prevalent in older populations.²⁶⁷ Finally, repeated exposure to interventions, especially those involving physical activity or cognitive engagement (eg, AR-VR), could lead to adaptation or learning effects that confound true treatment effects.

Conclusion

Wearables are revolutionizing motor rehabilitation by aiding precise, data-driven, and personalized interventions for individuals with movement disorders. These technologies have shown significant effectiveness in improving motor function, particularly gait, balance, and coordination, across neurological populations. Wearables enable tailored rehabilitation programs that address individual needs by integrating real-time biofeedback, rhythm-based therapies, and biomechanical systems. Their versatility spans both clinical and remote settings, with telerehabilitation expanding access to care for underserved populations and reducing barriers such as travel and clinic availability. Additionally, features like personalized auditory and visual cues, as well as adaptive AI-driven systems, further enhance engagement and adherence to wearable-based therapy. However, challenges remain in achieving sustained long-term outcomes, refining personalization to meet diverse user needs, and addressing issues of cost, accessibility, and usability. Despite some limitations, the growing body of evidence highlights the transformative potential of wearables to improve motor function, promote independence, and enhance the quality of life for individuals with movement disorders.

Funding

CW and JM are co-funded by a grant from the National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northeast and North Cumbria (NENC) in the UK. CW and JM are also co-funded by the Faculty of Environment and Engineering at Northumbria University.

Disclosure

The author(s) report no conflicts of interest in this work.

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