Sensor technologies have emerged as transformative tools in rehabilitation engineering, fundamentally changing how clinicians assess, monitor, and optimize therapeutic interventions for patients recovering from injuries, surgeries, and neurological conditions. Engineering has played a crucial role in improving the quality of lives of persons with disabilities as well as that of healthcare service providers, developing therapeutic interventions, assistive technologies, and objective monitoring methods to measure outcomes in the field of physical rehabilitation. These advanced sensing systems provide real-time, objective data that enables personalized treatment plans, continuous monitoring outside clinical settings, and evidence-based adjustments to rehabilitation protocols.
Wearable movement sensors can provide objective and precise measurements of the quantity and quality of physical activities, body postures, and movements in clinical as well as normal daily life environments, thereby providing clinicians with data that can be used to guide, personalize, and optimize therapy. The integration of sensor technologies into rehabilitation practice represents a paradigm shift from subjective clinical assessments to data-driven, quantifiable measurements that support better patient outcomes and more efficient healthcare delivery.
The Evolution and Importance of Sensor-Based Rehabilitation
The field of rehabilitation engineering has witnessed remarkable technological advancement in recent years, driven by innovations in sensor miniaturization, wireless connectivity, and data analytics. Since wearable sensors are portable, inexpensive, unobtrusive, and also have the ability to provide information that is unique and cannot be obtained otherwise (e.g., by standardized clinical tests or questionnaires), they have an enormous potential for the tracking of patient functioning and recovery during motor rehabilitation. This evolution has enabled rehabilitation professionals to move beyond traditional assessment methods that often rely on periodic clinical evaluations and patient self-reports.
Traditional methods, such as clinician-rated motor ability or patient self-reports, often fail to measure UE performance in real-life settings accurately. Evidence suggests that currently used clinical assessments do not reliably capture actual UE use at home or in the community. Sensor technologies address these limitations by providing continuous, objective monitoring of patient movement and function in both clinical and home environments.
There are many open challenges and opportunities to integrate engineering concepts into rehabilitation, not only reducing healthcare costs, but also increasing population wellbeing and wealth. This motivates researchers to conduct studies, design and develop novel rehabilitative and assistive technologies, and investigate methods to help people to monitor, improve, and recover cognitive and motor functions. The integration of sensors into rehabilitation practice supports the broader goals of improving accessibility, reducing treatment costs, and enabling more personalized care delivery.
Fundamental Design Considerations for Sensor Integration
Designing effective sensor systems for rehabilitation applications requires careful consideration of multiple factors that influence both clinical utility and patient acceptance. The success of sensor-based rehabilitation systems depends on balancing technical performance with practical usability in real-world settings.
Accuracy and Reliability
Sensor accuracy is paramount in rehabilitation applications where precise measurements inform clinical decisions and treatment adjustments. The use of wearable sensors to inform neurologic and orthopedic rehabilitation practice warrants careful consideration of their clinimetric properties, which vary among devices, conditions, measures, and environments. Information on reliability, validity, and sensitivity is available for some devices, but not all. Clinicians and engineers must carefully evaluate sensor specifications to ensure they meet the requirements of specific rehabilitation applications.
Different rehabilitation contexts demand varying levels of precision. For example, Impact magnitudes during running can often exceed 16gs, which is the limit of some commercial devices. Similarly, accelerations during running include high frequency components that require adequate sampling frequencies (500–1000hz). These factors need to be considered when choosing IMU-based devices for running studies. Understanding these technical requirements ensures that selected sensors can capture the full range of movements and forces relevant to specific therapeutic interventions.
Comfort and Wearability
Patient comfort and ease of use are critical factors that directly impact adherence to sensor-based rehabilitation programs. Sensors must be designed to be non-intrusive and adaptable to different body types, movement patterns, and daily activities. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. This trend toward better integration reflects growing recognition that sensor placement and attachment methods significantly influence both measurement accuracy and user acceptance.
Most included studies describe only superficially how to attach sensors on the human body, despite that the way this placement is done is very influential on both accuracy and comfort of the system. Proper sensor placement requires balancing biomechanical considerations with patient comfort, ensuring that devices remain securely positioned during movement while minimizing skin irritation and movement restriction.
Battery Life and Power Management
For continuous monitoring applications, battery life represents a crucial design consideration. In rehabilitation, an IMU system with long battery life can be effective in monitoring human motion in daily environments, providing supplementary information to laboratory tests. Extended battery life enables longer monitoring periods without interruption, providing more comprehensive data about patient function in real-world settings.
Potential challenges related to sensor battery life, data transmission, and user compliance must be carefully considered in real-world deployments. Prospective studies should evaluate sensor runtime under various sampling rates and data transmission schemes to identify optimal configurations that balance performance and power consumption. Advanced power management strategies can significantly extend operational time while maintaining measurement quality.
Compatibility and Integration
Sensor systems must integrate seamlessly with existing rehabilitation equipment, software platforms, and clinical workflows. Other health care systems may have inadequate infrastructure, which is a significant barrier to organizational implementation of connected sensor technology–augmented services. Ensuring compatibility with established healthcare information systems facilitates data sharing, clinical decision-making, and coordination among multidisciplinary rehabilitation teams.
Connected sensor technologies are defined as "technology products that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function". This definition emphasizes the importance of not just data collection, but also meaningful data processing and interpretation that supports clinical applications.
Types of Sensors Used in Rehabilitation Engineering
Modern rehabilitation engineering employs a diverse array of sensor technologies, each offering unique capabilities for measuring different aspects of human movement, muscle activity, and physiological function. Understanding the characteristics and applications of various sensor types enables clinicians and engineers to select appropriate technologies for specific rehabilitation needs.
Inertial Measurement Units (IMUs)
Inertial Measurement Units represent one of the most widely adopted sensor technologies in rehabilitation applications. Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. This widespread adoption reflects the versatility and effectiveness of IMUs for capturing movement data across diverse rehabilitation contexts.
At present, novel low-cost micro-electro-mechanical system (MEMS) inertial sensors have been widely used in Mocap as a cost-effective system. The inertial sensor usually consists of an accelerometer, gyroscope, magnetometer, and signal transmission chip. The IMUs have several advantages, such as low cost, customization, flexible application, and comfort in wearing. These characteristics make IMUs particularly suitable for both clinical and home-based rehabilitation applications.
IMUs are small, wearable sensors that track movement through accelerometers and gyroscopes. IMUs can be placed on various body parts to collect detailed movement data over extended periods, both in clinical settings and during daily activities. This capability for continuous monitoring in naturalistic environments provides insights into patient function that cannot be obtained through periodic clinical assessments alone.
There are already some successfully commercialized inertial Mocap systems, e.g., Noitom (NOITOM LTD.) and Xsens (Xsens Technologies B.V.). Many rehabilitation centers have used commercial off-the-shelf products and special applications for motion and gait analyses. The availability of commercial systems has accelerated the adoption of IMU-based monitoring in clinical practice, though custom solutions remain important for specialized applications.
Electromyography (EMG) Sensors
Electromyography sensors detect electrical activity produced by skeletal muscles, providing direct measurements of muscle activation patterns during movement and exercise. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. EMG sensors are particularly valuable for assessing muscle function, detecting fatigue, and monitoring neuromuscular recovery.
EMG technology enables clinicians to evaluate muscle recruitment patterns, identify compensatory movement strategies, and assess the effectiveness of strengthening exercises. These sensors can be surface-mounted or implanted, with surface EMG being more common in rehabilitation applications due to its non-invasive nature. The data collected from EMG sensors helps therapists understand whether patients are activating the correct muscles during therapeutic exercises and whether muscle function is improving over time.
These systems use multimodal data (motion capture, electromyography, and pressure sensors) to generate comprehensive assessments of patient function. The integration of EMG with other sensor modalities provides a more complete picture of movement quality and neuromuscular control.
Pressure Sensors
Pressure sensors measure force distribution and load patterns, making them essential for gait analysis, balance assessment, and monitoring weight-bearing activities. These sensors can be integrated into insoles, platforms, or wearable devices to capture pressure data during standing, walking, and other functional activities.
A custom body-worn sensor system comprised of force sensors and IMUs was used to provide kinematic biofeedback during gait training, leading to improvements in balance, mobility, strength, and range of motion that were comparable to the treatment benefits obtained through therapist-directed gait training. This demonstrates how pressure sensors, combined with other sensing modalities, can support effective therapeutic interventions.
Pressure sensing technology is particularly valuable for patients recovering from lower extremity injuries, those learning to use prosthetic devices, or individuals with balance impairments. By providing real-time feedback about weight distribution and loading patterns, pressure sensors help patients develop more symmetric and efficient movement patterns.
Goniometers and Joint Angle Sensors
Goniometers and other joint angle measurement devices quantify range of motion at specific joints, providing essential data for tracking recovery progress and ensuring exercises are performed through appropriate movement ranges. Traditional goniometers have evolved into electronic versions that can provide continuous measurement and data logging capabilities.
These sensors are crucial for monitoring joint mobility in patients recovering from orthopedic surgeries, managing arthritis, or rehabilitating from injuries that affect joint function. Continuous monitoring of joint angles during exercise helps ensure that patients are achieving therapeutic movement ranges while avoiding potentially harmful positions.
If additional IMUs are used on the upper limb and trunk, they can be used to estimate clinical parameters of interest during execution of functional tasks, such as elbow angle, shoulder angle, and trunk movement. Modern sensor systems can derive joint angle information from IMU data, providing an alternative to traditional goniometric measurements.
Emerging Sensor Technologies
Bioinspired electronic skins (e-skins), characterized by their lightweight, wearable, and multifunctional characteristics, present a viable pathway toward decentralized and continuous rehabilitation. Since their conceptual inception in the 1960s, e-skins have evolved through four distinct phases, culminating in today's precise, flexible, and highly integrated systems specifically designed for clinical applications. This review provides a systematic overview of the historical progression of this interdisciplinary field, categorizing five principal sensing mechanisms along with the corresponding fabrication strategies for e-skin technologies.
Advanced sensor technologies continue to emerge, offering new capabilities for rehabilitation monitoring. These include flexible sensors that conform to body contours, multi-modal sensors that capture multiple physiological parameters simultaneously, and smart textiles that integrate sensing capabilities directly into garments. Advancements in software, batteries, and sensors will allow for new and improved assistive technologies. In addition, the development of new materials and additive manufacturing techniques should advance the field of AT significantly.
Clinical Applications of Sensor Technologies in Rehabilitation
Sensor technologies have found widespread application across diverse rehabilitation contexts, from neurological recovery to orthopedic rehabilitation and chronic disease management. These applications demonstrate the versatility and clinical value of sensor-based monitoring and intervention systems.
Gait Analysis and Mobility Assessment
Gait analysis represents one of the most established applications of sensor technology in rehabilitation. Gait analysis and mobility tracking have been central to wearable sensor applications, particularly in neurological, musculoskeletal, and age-related conditions. In Parkinson disease (PD), wearable sensors have been used to detect subtle changes in gait speed, stance and swing phase durations, and postural instability, aiding in early disease detection and progression monitoring. In stroke rehabilitation, these sensors enable remote motor recovery assessment and provide continuous mobility data outside traditional clinical settings.
Tracked features of turning performance (e.g., velocity, duration, and step number) for seven days, revealing a high correlation between disease severity and turning mobility. Additional studies have shown that IMU-enabled continuous monitoring of baseline gait metrics can predict disease progression and gait decline 1- and 2-years later. Moreover, a recent large study of 190 patients with PD and 101 age-matched controls shows the feasibility for large-scale clinical trials to use IMUs to robustly track spatiotemporal parameters of gait. This predictive capability enables proactive intervention and more personalized treatment planning.
Sensor-based gait analysis provides detailed information about stride length, cadence, gait symmetry, and other parameters that are difficult to assess through visual observation alone. This objective data helps clinicians identify subtle gait abnormalities, track recovery progress, and evaluate the effectiveness of interventions designed to improve walking ability.
Upper Limb Rehabilitation and Training
This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Upper extremity rehabilitation benefits significantly from sensor-based monitoring and feedback systems.
In stroke rehabilitation, IMU-based movement quality assessments achieved 92% accuracy (F1-score=0.95), supporting their use for personalized therapy and remote monitoring. Recent studies have also extended the application of IMU-based wearable sensors to shoulder rehabilitation. This high accuracy demonstrates the reliability of sensor-based assessments for guiding therapeutic interventions.
Tranquilli et al demonstrated that a single IMU could simultaneously capture joint mobility and muscle strength dynamics during postinjury recovery. Ajčević et al applied IMU sensors to quantify shoulder kinematics and evaluate therapeutic response in adhesive capsulitis, whereas Parel et al introduced a kinematic biofeedback program integrating inertial sensors for patients after rotator cuff repair. These studies highlight the versatility of IMU technology for upper-limb functional assessment and real-time feedback during rehabilitation.
Balance Assessment and Fall Prevention
Balance impairment and fall risk represent significant concerns for many rehabilitation populations, including older adults, individuals with neurological conditions, and those recovering from orthopedic injuries. Sensor technologies provide objective methods for assessing balance control and identifying individuals at elevated fall risk.
In PD monitoring, wearable technology achieved an accuracy of 83% in tremor detection and both a sensitivity and specificity of 86% in fall risk prediction, supporting the feasibility of early intervention strategies. This capability for early risk identification enables proactive interventions that may prevent falls and associated injuries.
Sensor-based balance assessment can capture subtle postural instabilities that may not be apparent during standard clinical tests. Continuous monitoring in home environments provides information about balance control during daily activities, offering insights into real-world fall risk that complement clinic-based assessments.
Stroke Rehabilitation
Stroke rehabilitation represents a major application area for sensor technologies, with systems designed to monitor motor recovery, provide therapeutic feedback, and enable remote rehabilitation delivery. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14).
Dr. Marika Demers from the Université de Montreal, who focused on gathering feedback from key stakeholder groups in the user-centered design process for wearable sensors for stroke rehabilitation: patients (people living with stroke), clinicians, and researchers and other experts. Overall, stroke survivors indicated enthusiasm for using wearable technology to support their home-based rehabilitation, and they were able to offer helpful feedback on their motivations and goals, the usefulness of real-time feedback, and the need for continuing engagement with clinicians for exercise prescriptions and to ensure accountability.
Wearable sensors have also enabled novel gait training approaches, such as biofeedback-based interventions. For example, a custom body-worn sensor system comprised of force sensors and IMUs was used to provide kinematic biofeedback during gait training, leading to improvements in balance, mobility, strength, and range of motion that were comparable to the treatment benefits obtained through therapist-directed gait training. These results demonstrate the potential for wearable sensors to provide effective gait intervention without direct oversight by a clinician (e.g., in real world settings).
Orthopedic and Musculoskeletal Rehabilitation
For rehabilitation assessment, wearable sensors improved postsurgical monitoring in patients who underwent knee arthroplasty, showing 52% better bone stimulus and 371% better impact load tracking. This demonstrates the value of sensor-based monitoring for tracking recovery following orthopedic procedures.
Sensor technologies support rehabilitation following joint replacements, ligament repairs, fractures, and other musculoskeletal injuries. By providing objective data about movement patterns, loading forces, and range of motion, these systems help ensure that patients progress safely through rehabilitation protocols while avoiding movements that could compromise healing.
Wearable sensors present an exciting opportunity both in the prevention and treatment of running-related injuries by affording the ability to provide real-time feedback to the individual. Many of the commercial IMUs provide information on cumulative loads which can be extremely helpful in preventing overload injuries in runners. This application extends beyond traditional rehabilitation to injury prevention in athletic populations.
Parkinson's Disease Management
Like in stroke, AR algorithms have enabled IMU data to be used to identify pathological motor features characteristic of PD. For example, periods of motor fluctuations between mobile and immobile states (i.e., on-off periods) in levodopa-treated individuals were detected using IMU data analyzed with an advanced AR algorithm. Sensor technologies provide valuable tools for monitoring disease progression and medication effectiveness in Parkinson's disease.
Like in stroke, sensor-enabled biofeedback interventions have gained popularity as noninvasive training tools in PD rehabilitation. For example, wearable sensors have been used to facilitate the delivery of rhythmic auditory or haptic cues during gait training, an approach shown to enhance motor learning in persons with PD. These interventions leverage sensor technology to provide precisely timed cues that help patients overcome movement difficulties characteristic of Parkinson's disease.
Cardiac and Pulmonary Rehabilitation
Wearable sensors for cardiac rehabilitation demonstrated reliable 6-minute walk distance prediction, with an error of 42.8 m and an R2 value of 0.661, facilitating remote patient monitoring. Sensor technologies extend beyond musculoskeletal and neurological applications to support cardiac and pulmonary rehabilitation programs.
The fusion of a wearable device and a motion-sensing exercise monitoring system for pulmonary rehabilitation was intended to measure biomedical data such as heart rate and breathing rate while the patient followed coaching videos for performing physical activity. Data collected from the exercise movements and heart rate of the patient were automatically stored and synchronized with the health management server. Remotely, a medical professional could consult and analyze (online or later) the evolution of the patient's health status. This integration of physiological monitoring with movement tracking provides comprehensive assessment of patient response to therapeutic exercise.
Biofeedback and Real-Time Intervention Systems
One of the most powerful applications of sensor technology in rehabilitation involves providing real-time biofeedback to patients during therapeutic exercises. This immediate feedback helps patients learn correct movement patterns, maintain proper form, and achieve therapeutic goals more effectively.
16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. Different feedback approaches serve distinct therapeutic purposes, with knowledge of results informing patients about outcomes achieved and knowledge of performance providing information about movement quality.
Given the range of gait characteristics that can be measured (e.g., strike pattern, lower extremity angles, tibial shock, etc.) a wide variety of gait deviations can be addressed. Once the faulty aspect of gait is identified by the physical therapist, the runner can be instructed in how to alter the gait pattern. The therapist can then set audible signals to remind the patient to attend to their gait when it begins to degrade beyond a certain threshold. Feedback can then be gradually removed with time. This systematic approach to feedback delivery supports motor learning and skill retention.
Recent advances in smartphone technology such as their prevalence, ability to use anywhere, powerful processing ability and integration of sensor and display have had a major impact on their use in rehabilitation systems. Providing feedback like visual information on smartphones is common and effective, especially for the systems intended for remote monitoring. The ubiquity of smartphones has made sophisticated biofeedback systems more accessible and user-friendly.
Home-Based and Telerehabilitation Applications
The ability to monitor patient function and deliver therapeutic interventions outside traditional clinical settings represents a transformative application of sensor technology. Home-based rehabilitation supported by sensor monitoring addresses barriers related to transportation, scheduling, and access to specialized services.
Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program. This capability enables clinicians to verify that patients are performing prescribed exercises correctly and adhering to treatment recommendations.
A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants' recordings was assessed with metrics of movement smoothness, intensity, consistency and control. In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. This research demonstrates the feasibility of unsupervised exercise monitoring using wearable sensors.
Previous studies have shown that a combination of in-home set of exercises involving everyday life functional movements such as fast and slow walking, going up and down the stairs, standing up and sitting down, turning, and crossing the obstacles and some specially designed activities for each patient with clinical approaches such as LSVT BIG program would empower the rehabilitation treatment. Therefore, an automated system that can recognize such functional activities at home and guide the patient accordingly using audio/visual feedback and instructions would be of great interest to people with PD and other movement disorders, and can be widely used as a platform for tele-rehabilitation by the therapists.
Clinicians supported the use of wearable sensors to collect objective measures of real-life activity and functioning of their community-dwelling patients. They also valued the data for guiding clinical decisions, supporting patient self-management and engagement, and promoting energy management and personalized care approaches.
Data Analytics and Machine Learning Integration
The vast amounts of data generated by sensor systems require sophisticated analytical approaches to extract clinically meaningful information. Machine learning and artificial intelligence have become integral to modern sensor-based rehabilitation systems, enabling automated pattern recognition, predictive analytics, and personalized treatment recommendations.
Prior research has demonstrated the potential of accelerometry data combined with machine learning algorithms to classify and analyze UE movements. Studies have shown that IMUs can effectively capture the nuances of arm movements, providing valuable insights into motor function and the progress of rehabilitation. These analytical capabilities transform raw sensor data into actionable clinical insights.
Machine learning has also been widely used in upper extremity (UE) assessment to provide objective, scalable, and quantifiable measures of motor function, offering more accurate evaluations compared to traditional methods. Techniques such as Random Forest (RF), Support Vector Machines, Logistic Regression Classifiers, and traditional neural networks have been employed to classify functional and non-functional movements from sensor data, often acquired with IMUs or accelerometers, to monitor stroke recovery progress. These models have the potential to automate the assessment process, reduce reliance on subjective clinical evaluations, and enable continuous monitoring of patient function.
The latest trend involves integrating motor and sensory rehabilitation into integrated platforms powered by AI. This integration enables more comprehensive assessment and treatment approaches that address multiple aspects of patient function simultaneously.
IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
Personalized Rehabilitation and Adaptive Interventions
Sensor technologies enable unprecedented levels of personalization in rehabilitation interventions. By continuously monitoring patient performance and progress, sensor-based systems can automatically adjust exercise difficulty, provide individualized feedback, and optimize treatment parameters for each patient's unique needs and capabilities.
Dr. James Rehg reviewed wearable sensor technologies to capture data that measure target health indicators to support just-in-time adaptive interventions (JITAIs, a key feature of precision rehabilitation). These adaptive interventions deliver therapeutic support precisely when patients need it, based on real-time assessment of their status and context.
Zhou et al. (2023) developed an AI-guided robotic arm rehabilitation system that adapts in real-time to patient fatigue and motor performance. The study showed a 28% improvement in Fugl-Meyer Assessment (FMA) scores at 8 weeks compared to traditional therapy. This demonstrates the clinical benefits of adaptive, sensor-driven rehabilitation systems.
Some of the highlighted aims include the quantification of patients' exercise data, providing personalized rehabilitation services, reducing muscle load, and making walking more efficient. The results of these studies have demonstrated considerable progress in exoskeleton designs, sensors' integration, and control algorithms, leading to improved exoskeleton functionality and user experiences. Personalization extends to robotic rehabilitation devices that adjust assistance levels based on patient performance.
Integration with Rehabilitation Robotics and Exoskeletons
Sensor technologies play a critical role in rehabilitation robotics and exoskeleton systems, providing the sensory information needed for responsive control and safe human-robot interaction. These integrated systems combine mechanical assistance with intelligent sensing to support motor recovery and functional training.
Rehabilitation using high technology digital devices is a leading-edge advance in rehabilitation medicine. Using robot-assisted fatigue-free training, we can provide optimal task-specific, goal-oriented, and intense motor training. Robots can also be used for motor assistance and compensate for the impaired function as a type of orthosis or assistive device. Sensor integration enables these robotic systems to respond appropriately to patient movements and intentions.
It concentrates on a systematic classification and description of exoskeleton hardware research, including the use of functional actuation, advanced epidermal electronic sensors and monitoring systems, innovative joint structures, and user-driven controller design. Advanced sensor technologies enable more sophisticated control strategies that improve the naturalness and effectiveness of robotic assistance.
They can improve walking mechanics, provide high-intensity training outside the clinic, and can be designed to be adjustable to accommodate a range of physical abilities. Sensor-equipped robotic devices extend the reach of intensive rehabilitation interventions beyond traditional clinical settings.
Clinical Validation and Evidence Base
While sensor technologies show tremendous promise for rehabilitation applications, rigorous clinical validation remains essential for establishing their effectiveness and guiding clinical adoption. Research continues to build the evidence base supporting sensor-based assessment and intervention approaches.
16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial. This highlights the need for more rigorous clinical trials to establish the efficacy of sensor-based rehabilitation interventions.
Observational studies accounted for 43% (13/30) of the reviewed studies, reflecting the feasibility of longitudinal monitoring, whereas experimental studies made up 27% (8/30), playing a crucial role in validating sensor-based assessments. However, the limited number of RCTs, representing only 10% (3/10) of the studies, underscores the need for rigorous intervention-based research to establish clinical effectiveness.
A group of 100 patients after stroke underwent a 12-week AI-driven rehabilitation program. Motor and sensory functions were quantitatively assessed using the Fugl-Meyer Assessment (FMA) and Semmes-Weinstein Monofilaments (SWM) before and after the intervention. Subjective assessments further confirmed improvements in daily function, limb sensitivity, and coordination. Such studies provide important evidence supporting the clinical benefits of sensor-based rehabilitation approaches.
Barriers and Challenges to Clinical Implementation
Despite the significant potential of sensor technologies in rehabilitation, several barriers continue to limit widespread clinical adoption. Understanding and addressing these challenges is essential for realizing the full benefits of sensor-based rehabilitation systems.
Lang et al. (2020) discussed the major barriers for the application of wearable movement sensors in motor rehabilitation and proposed benchmarks for the implementation of sensors in clinical practice. Barriers in the clinic are the busy clinical environment and the lack of realization of the value of the information that can be obtained with sensors. Technology-related barriers include: (1) sensor systems that are inaccurate for many patient populations; (2) sensor systems that are not user-friendly for clinicians and/or patients; (3) the lack of published data regarding reliability and clinical validity of sensor systems.
Braakhuis et al. (2021) explored the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Results of the online survey showed that 27% of the respondents were using activity monitoring, and the concept of remote activity monitoring was perceived as useful. This suggests that while clinicians recognize the value of sensor-based monitoring, adoption remains limited.
Challenges and concerns were also identified, particularly by R&D experts, who indicated concern about whether existing technologies are adequately mature, technological complexity, privacy and security, adoption by clinicians, and reimbursement by payers. These multifaceted challenges require coordinated efforts from technology developers, clinicians, healthcare administrators, and policymakers.
But, it is still hard to satisfy both functionality and adoptability for innovative technologies, including rehabilitation robotics and sensor technologies. Many engineers and clinicians are working hard to push it to a feasible and affordable level. Ongoing collaboration between engineers and clinicians is essential for developing sensor systems that meet both technical and practical requirements.
User-Centered Design and Stakeholder Engagement
Successful implementation of sensor technologies in rehabilitation requires careful attention to the needs and preferences of all stakeholders, including patients, clinicians, caregivers, and healthcare administrators. User-centered design approaches ensure that sensor systems are acceptable, usable, and valuable to those who will use them.
The RERC must consider and optimize the accessibility, usability, utility, and acceptability of the technological devices that it focuses upon. The RERC must involve children with orthopedic disabilities, their families, caretakers, and other key stakeholders in the design and implementation of RERC research and development activities. This principle of stakeholder engagement applies across all rehabilitation populations and applications.
Most articles have conducted a technical, a usability, and more rarely a clinical evaluation (only 3), while none of the included studies report a systematic wearability assessment, which is quite essential for user acceptance. Most included studies describe only superficially how to attach sensors on the human body, despite that the way this placement is done is very influential on both accuracy and comfort of the system. Greater attention to wearability and user experience is needed to improve adoption and adherence.
Strategies to maximize user adherence include implementing calibration routines to ensure data quality, wear-time monitoring to track device usage, and providing timely user feedback to encourage consistent wear. These practical considerations significantly influence the real-world effectiveness of sensor-based rehabilitation systems.
Data Privacy, Security, and Ethical Considerations
As sensor technologies collect increasingly detailed information about patient movement, function, and health status, ensuring appropriate data privacy and security becomes paramount. Rehabilitation professionals and technology developers must address ethical considerations related to data collection, storage, sharing, and use.
Sensor systems often collect continuous data streams that may reveal sensitive information about patient activities, locations, and health conditions. Robust security measures must protect this data from unauthorized access while enabling appropriate sharing with healthcare providers and researchers. Clear policies regarding data ownership, consent, and use help ensure that sensor-based monitoring respects patient autonomy and privacy rights.
Transparency about what data is collected, how it will be used, and who will have access to it builds trust and supports informed decision-making by patients and families. As sensor technologies become more sophisticated and data analytics more powerful, ongoing attention to ethical implications remains essential.
Future Directions and Emerging Trends
The field of sensor-based rehabilitation continues to evolve rapidly, with several emerging trends pointing toward future developments that will further enhance clinical capabilities and patient outcomes.
The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings. Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.
Advanced power management techniques—such as adaptive sampling, low-power modes, and efficient data communication protocols—can significantly extend battery life in wearable devices. Future work should integrate these approaches and explore energy-harvesting technologies to sustain longer operational times, ensuring reliability and user convenience during continuous monitoring in clinical and home environments. Technological advances will continue to improve the practicality and capabilities of sensor systems.
Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. Ongoing research continues to expand understanding of how sensor technologies can best support rehabilitation across diverse populations and conditions.
However, these studies also highlight certain limitations and areas for future research, such as verifying the stability and feasibility of the prototypes, improving the sensing and control methods, enhancing the balance and torque capacities, and expanding the subject populations to ensure the effectiveness of these devices in clinical and geriatric settings. Continued refinement of sensor technologies and control algorithms will enhance their clinical utility.
Multimodal Sensor Integration
Future sensor systems will increasingly integrate multiple sensing modalities to provide more comprehensive assessment of patient function. Combining kinematic data from IMUs with muscle activity from EMG sensors, force data from pressure sensors, and physiological measurements creates a more complete picture of patient status and response to therapy.
Wearable sensor technology, including IMU-based smartwatches, smart insoles, and multisensor systems, has significantly transformed health monitoring, rehabilitation tracking, and disease risk assessment. These devices enable continuous, real-world tracking of mobility and functional health, addressing key limitations of traditional clinical assessments. The reviewed studies highlight these devices' diverse applications in neurological, musculoskeletal, cardiovascular, and metabolic conditions, supporting early disease detection, remote therapy adherence, and precision rehabilitation.
Advanced Analytics and Artificial Intelligence
Continued advances in machine learning and artificial intelligence will enable more sophisticated analysis of sensor data, supporting better prediction of outcomes, earlier detection of complications, and more precise personalization of interventions. These analytical capabilities will help translate the vast amounts of data generated by sensors into actionable clinical insights.
These devices, including IMU-based sensors, smartwatches, and multisensor systems, have transformed traditional gait and activity monitoring by enabling remote, noninvasive tracking of movement patterns and health status. The integration of advanced analytics, particularly machine learning (ML), has further enhanced their diagnostic and predictive capabilities, positioning wearable sensors as key tools in digital health and precision medicine.
Improved Integration with Healthcare Systems
Future developments will focus on better integration of sensor data with electronic health records and clinical decision support systems. Seamless data flow between sensor systems and healthcare information infrastructure will enable more efficient clinical workflows and better coordination of care across providers and settings.
For example, the Veterans Health Administration (VHA) has been and continues to be a leader in deploying technology solutions for health care. The VHA Telehealth Services program office was established over 20 years ago and has developed necessary infrastructure to support rehabilitation practices that leverage remote patient monitoring to inform care decisions. Healthcare systems that invest in appropriate infrastructure can more effectively leverage sensor technologies for rehabilitation.
Expansion to New Clinical Populations
While much current research focuses on adult populations with neurological and orthopedic conditions, sensor technologies are increasingly being applied to pediatric rehabilitation, geriatric care, and management of chronic diseases. Continued research and development into these rehabilitation technologies is needed to expound their effectiveness and optimize their integration into pediatric rehabilitation programs. Assistive technologies (AT) are instrumental for the development and participation of children with disabilities. They span a wide range of tools and solutions and have been shown to increase autonomy, enhance engagement, improve social interaction, and build confidence.
Best Practices for Implementing Sensor Technologies
For clinicians and healthcare organizations considering implementation of sensor technologies in rehabilitation practice, several best practices can support successful adoption and effective use.
Start with Clear Clinical Goals
Implementation should begin with clearly defined clinical objectives that sensor technology will help achieve. Whether the goal is improving gait symmetry, increasing exercise adherence, or enabling remote monitoring, having specific targets guides technology selection and implementation strategies.
Involve Stakeholders Early
Engaging patients, clinicians, and other stakeholders from the beginning of implementation planning helps ensure that selected technologies meet real needs and fit within existing workflows. User feedback during pilot testing can identify issues before full-scale deployment.
Provide Adequate Training and Support
Both clinicians and patients require appropriate training to use sensor systems effectively. Ongoing technical support helps address issues that arise during routine use and maintains confidence in the technology.
Establish Clear Protocols
Developing standardized protocols for sensor placement, data collection, interpretation, and clinical decision-making based on sensor data helps ensure consistent, high-quality implementation across patients and providers.
Monitor Outcomes and Iterate
Systematic evaluation of clinical outcomes, user satisfaction, and implementation challenges supports continuous improvement. Being willing to adjust approaches based on experience and feedback enhances long-term success.
Conclusion
Sensor technologies have fundamentally transformed rehabilitation engineering, providing unprecedented capabilities for objective assessment, continuous monitoring, personalized intervention, and remote care delivery. From IMUs that track movement patterns to EMG sensors that measure muscle activity, these technologies enable clinicians to gather detailed, quantitative data that supports evidence-based treatment decisions and optimized therapeutic outcomes.
The integration of sensors into rehabilitation practice addresses longstanding limitations of traditional assessment methods, providing objective measurements of patient function in both clinical and real-world settings. Applications span diverse rehabilitation contexts, including stroke recovery, orthopedic rehabilitation, Parkinson's disease management, balance training, and cardiac rehabilitation. Sensor-based biofeedback systems enhance motor learning, while home monitoring capabilities extend the reach of therapeutic interventions beyond traditional clinical boundaries.
Despite significant progress, challenges remain in areas including clinical validation, user acceptance, data privacy, healthcare system integration, and reimbursement. Addressing these barriers requires continued collaboration among engineers, clinicians, patients, researchers, and policymakers. User-centered design approaches that prioritize the needs and preferences of all stakeholders will be essential for developing sensor systems that are not only technically sophisticated but also practical, acceptable, and valuable in real-world clinical practice.
Looking forward, emerging trends including multimodal sensor integration, advanced artificial intelligence analytics, improved healthcare system integration, and expansion to new clinical populations promise to further enhance the capabilities and impact of sensor-based rehabilitation. As technologies continue to evolve and evidence bases expand, sensor systems will play an increasingly central role in delivering personalized, effective, and accessible rehabilitation services.
For rehabilitation professionals, staying informed about sensor technologies and their applications represents an important aspect of contemporary practice. Understanding the capabilities, limitations, and appropriate uses of different sensor systems enables clinicians to make informed decisions about technology adoption and integration. As the field continues to advance, sensor technologies will remain essential tools for optimizing patient outcomes and advancing the science and practice of rehabilitation engineering.
The successful integration of sensor technologies into rehabilitation practice ultimately depends on maintaining focus on patient-centered care, clinical effectiveness, and practical implementation. By leveraging these powerful tools thoughtfully and systematically, rehabilitation professionals can enhance their ability to help patients achieve meaningful functional improvements and improved quality of life.
For more information on rehabilitation engineering and assistive technologies, visit the Rehabilitation Engineering and Assistive Technology Society of North America (RESNA). Additional resources on wearable sensor technologies can be found through the Institute of Electrical and Electronics Engineers (IEEE). Clinical guidelines and evidence-based practices are available from the American Physical Therapy Association. Research on sensor applications in rehabilitation is published in journals such as the Journal of NeuroEngineering and Rehabilitation and Sensors.