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Sensors and Wearables for Rehabilitation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 13020

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Special Issue Editors

Prof. Dr. Natalie Baddour

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Guest Editor

Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada
Interests: wearable sensors; mHealth; signal processing; mobility
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Edward Lemaire

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Guest Editor

Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
Interests: mobility; assistive technology; wearable sensors; mHealth; multimedia; disability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Physical rehabilitation is an essential component of the healthcare system, enabling people to move beyond their deficits to achieve their goals, maintain their quality of life, and engage with their community. Understanding a person’s mobility status, mental state, etc., is essential for providing informed and high-quality rehabilitation. Wearable sensors are becoming important tools to achieve this understanding, especially since data can be collected outside the clinic or lab to provide quantitative information about an individual’s chosen environment.

This Special Issue accepts submissions of high-quality research papers and review articles in the topic area of wearable sensor applications for rehabilitation. Submissions must be original contributions that have not been published before and are not currently under review by other journals.

Potential topics of interest include, but are not limited to, the following:

Wearable sensors systems for outcome measurement;
Machine learning approaches to extract knowledge from wearable sensor systems;
Embedded sensor systems in wearable assistive devices (such as prostheses or orthoses);
Wearable sensor systems to characterize mental state;
Sensor approaches to enhance current clinical outcome measures;
Smartphone applications related to rehabilitation, employing sensor or imaging approaches.

Prof. Dr. Natalie Baddour
Prof. Dr. Edward Lemaire
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

wearable sensors
rehabilitation
mobility
mHealth
assistive technology
smartphones
machine learning
artificial intelligence

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Published Papers (10 papers)

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Research

15 pages, 4161 KiB

Open AccessArticle

A Monocular Camera as an Operation Logger for Motorized Mobility Scooters: Visual Odometry Method to Estimate Steering and Throttle Angles

by Naoto Haraguchi, Yi Liu, Haruki Sugiyama, Kazunori Hase and Jun Suzurikawa

Sensors 2025, 25(9), 2701; https://doi.org/10.3390/s25092701 - 24 Apr 2025

Viewed by 94

Abstract

Motorized mobility scooters (MMSs) are vital assistive technology devices that facilitate independent living for older adults. In many cases, older adults with physical impairments operate MMSs without special licenses, increasing the risk of accidents caused by operational errors. Although sensing systems have been developed to record MMS operations and evaluate driving skills, they face challenges in clinical applications because of the complexity of installing inertial measurement units (IMUs). This study proposes a novel recording system for MMS operation that uses a compact single-lens camera and image processing. The system estimates steering and throttle angles during MMS operation using optical flow and template matching approaches. Estimation relies on road surface images captured by a single monocular camera, significantly reducing the complexity of the sensor setup. The proposed system successfully estimated the steering angle with comparable accuracy to existing approaches using IMUs. Estimation of the throttle angle was negatively affected by the inertia of the MMS body during acceleration and deceleration but demonstrated high accuracy during stable driving conditions. This method provides a fundamental computational technique for measuring MMS operations using camera images. With its simple setup, the proposed system enhances the usability of recording systems for evaluating MMS driving skills. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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14 pages, 8575 KiB

Open AccessArticle

3D-Printed Insole for Measuring Ground Reaction Force and Center of Pressure During Walking

by Le Tung Vu, Joel Bottin-Noonan, Lucy Armitage, Gursel Alici and Manish Sreenivasa

Sensors 2025, 25(8), 2524; https://doi.org/10.3390/s25082524 - 17 Apr 2025

Viewed by 463

Abstract

Ground reaction force (GRF) and center of pressure (COP) during walking are two important measures that could be used in a range of applications, from the control of devices such as exoskeletons to clinical assessments. Recording these measures requires fixed laboratory equipment such as force plates or expensive portable insoles. We present an alternative approach by developing a 3D-printed insole that uses pneumatic chambers and pressure sensors to estimate the net GRF and the anterior–posterior COP position. The intentionally simple design, using just two pneumatic chambers, can be fabricated using standard 3D printing technology and readily available soft materials. We used experimentally recorded data from a motion capture system along with parameter identification techniques to characterize and validate the insole while walking at different speeds. Our results showed that the insole was capable of withstanding repeated loading during walking—up to 1.2 times the body weight—and possessed a bandwidth high enough to capture gait dynamics. The identified models could estimate the GRF and the anterior–posterior COP position with less than 9% error. These results compare favourably with those of commercially available instrumented insoles and can be obtained at a fraction of their cost. This low-cost yet effective solution could assist in applications where it is important to record gait outside of laboratory conditions, but the cost of commercial solutions is prohibitive. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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13 pages, 1466 KiB

Open AccessArticle

Application of Smart Watch-Based Functional Evaluation for Upper Extremity Impairment: A Preliminary Study on Older Emirati Stroke Population

by Yeo Hyung Kim, Sarah Kim and Hyung Seok Nam

Sensors 2025, 25(5), 1554; https://doi.org/10.3390/s25051554 - 3 Mar 2025

Viewed by 619

Abstract

Smartwatch-based functional assessments for upper extremity movement are a promising tool for a detailed and serial assessment during stroke rehabilitation, but their clinical application remains challenging. In this study, nine patients with hemiparesis due to a stroke participated in occupational therapy sessions using virtual reality-based rehabilitation devices. An Action Research Arm Test (ARAT) was performed at baseline and after intervention, with wrist smartwatch sensors recording motion data. We extracted acceleration and gyro sensor data from smartwatches and calculated the average motion segment size (MSS) as a measure of motion smoothness. Among the included patients, four participants completed all 10 therapy sessions and the follow-up evaluation. The average MSSs of acceleration for all x, y, and z directions were significantly correlated with the ARAT scores across all task domains. For angular motion, the average MSS in the gross movement task (domain 4) showed strong correlations with the ARAT scores: roll (r_s = 0.735, p = 0.004), pitch (r_s = 0.715, p = 0.009), and yaw (r_s = 0.704, p = 0.007). At the serial follow-ups, most participants showed a considerable increase in the average MSSs of the roll, pitch, and yaw angles measured during domain 4, alongside improvements in their clinical ARAT scores. Our findings support the feasibility of using commercial smartwatch-based parameters for upper extremity functional evaluations during stroke rehabilitation and highlight their potential for serial follow-up assessments. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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12 pages, 560 KiB

Open AccessArticle

Targeted Biofeedback Training to Improve Gait Parameters in Subacute Stroke Patients: A Single-Blind Randomized Controlled Trial

by Dmitry V. Skvortsov, Sergey N. Kaurkin and Galina E. Ivanova

Sensors 2024, 24(22), 7212; https://doi.org/10.3390/s24227212 - 11 Nov 2024

Viewed by 1337

Abstract

Biofeedback (BFB) is a rehabilitation method, which, among other things, is used for the restitution of motor and gait function. As of now, it has become technically feasible to use BFB training based on target gait parameters to improve the gait function in stroke patients. The walking patterns of stroke patients are generally characterized by significant gait phase asymmetries, mostly of the stance phase and the single stance phase. The aim of the study was to investigate the restoration of gait function using BFB training with gait phases as feedback targets. The study included two patient groups, each of 20 hemiparetic patients in the subacute stage of stroke and a control group of 20 healthy subjects. Each patient group received BFB training with either stance phase or single stance phase as the feedback target, respectively. The patients received a total of 8 to 11 training sessions. Assessments based on clinical scales and gait analysis data (spatiotemporal, kinematic, and EMG parameters) were performed before and after the training course. The score-based clinical assessments showed a significant improvement in both patient groups. According to the assessments of gait biomechanics, the subjects in the Single Stance Phase group had significantly more severe dysfunctions. In both patient groups, the unaffected limb responded to the BFB training, while the stance phase significantly changed after training in the unaffected limb only. The other patient group, trained using the single stance phase as the feedback target, showed no changes in the target parameter either in the affected or in the contralateral limb. The clinical and instrumental assessments showed different, non-equivalent sensitivity. The results of the study demonstrated the possibility to use targeted BFB training to improve walking function. However, a significant effect of such training was only observed with stance phase as the target parameter. A response to training was observed predominantly in the unaffected limb and facilitated the desired increase in the functional ability of the paretic limb. Training based on stance phase as the target parameter is probably preferable for the patient population under study. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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20 pages, 8649 KiB

Open AccessArticle

Real-Time Sensing of Upper Extremity Movement Diversity Using Kurtosis Implemented on a Smartwatch

by Guillem Cornella-Barba, Shusuke Okita, Zheng Li and David J. Reinkensmeyer

Sensors 2024, 24(16), 5266; https://doi.org/10.3390/s24165266 - 14 Aug 2024

Cited by 1 | Viewed by 1457

Abstract

Wearable activity sensors typically count movement quantity, such as the number of steps taken or the number of upper extremity (UE) counts achieved. However, for some applications, such as neurologic rehabilitation, it may be of interest to quantify the quality of the movement experience (QOME), defined, for example, as how diverse or how complex movement epochs are. We previously found that individuals with UE impairment after stroke exhibited differences in their distributions of forearm postures across the day and that these differences could be quantified with kurtosis—an established statistical measure of the peakedness of distributions. In this paper, we describe further progress toward the goal of providing real-time feedback to try to help people learn to modulate their movement diversity. We first asked the following: to what extent do different movement activities induce different values of kurtosis? We recruited seven unimpaired individuals and evaluated a set of 12 therapeutic activities for their forearm postural diversity using kurtosis. We found that the different activities produced a wide range of kurtosis values, with conventional rehabilitation therapy exercises creating the most spread-out distribution and cup stacking the most peaked. Thus, asking people to attempt different activities can vary movement diversity, as measured with kurtosis. Next, since kurtosis is a computationally expensive calculation, we derived a novel recursive algorithm that enables the real-time calculation of kurtosis. We show that the algorithm reduces computation time by a factor of 200 compared to an optimized kurtosis calculation available in SciPy, across window sizes. Finally, we embedded the kurtosis algorithm on a commercial smartwatch and validated its accuracy using a robotic simulator that “wore” the smartwatch, emulating movement activities with known kurtosis. This work verifies that different movement tasks produce different values of kurtosis and provides a validated algorithm for the real-time calculation of kurtosis on a smartwatch. These are needed steps toward testing QOME-focused, wearable rehabilitation. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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14 pages, 2180 KiB

Open AccessArticle

EFisioTrack System for Monitoring Therapeutic Exercises in Patients with Shoulder Orthopedic Injuries in a Hospital Setting: A Pilot Feasibility Study

by Sergio Hernandez-Sanchez, Jorge Roses-Conde, Neus Martinez-Llorens, Daniel Ruiz, Luis Espejo-Antúnez, Isabel Tomás-Rodríguez, Jose-Vicente Toledo-Marhuenda and Manuel Albornoz-Cabello

Sensors 2024, 24(15), 4898; https://doi.org/10.3390/s24154898 - 28 Jul 2024

Viewed by 1204

Abstract

To assess the effects of the eFisioTrack monitoring system on clinical variables in patients with prescribed physiotherapy for shoulder injuries, twenty-four adult patients with shoulder orthopaedic injuries who underwent physical therapy treatment in a hospital setting participated in the study (twelve in the experimental group and twelve as controls). Clinical outcome measures were shoulder function and pain (Constant–Murley Score and Disabilities of the Arm, Shoulder, and Hand or DASH score). Each variable was measured by a blinded physiotherapist at baseline and at one month follow-up. Patients performed the prescribed exercises either supervised by the physiotherapist (control group) or in a separate room without therapist supervision (experimental group). There were no statistically significant differences between groups before treatment or at follow-up for any outcomes (p ≥ 0.05). There was a statistically significant decrease (p ≤ 0.05) of at least 10 points in both groups for the DASH score at follow-up. Differences in the total score and subjective components of the Constant–Murley were also evidenced within groups. The use of the eFisioTrack system showed similar results in clinical measures compared to those performed under the direct supervision of the physiotherapist. This approach might be suitable for providing an effective shoulder exercise program at home. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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12 pages, 2949 KiB

Open AccessArticle

The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type

by Rienk van der Slikke, Arie-Willem de Leeuw, Aleid de Rooij and Monique Berger

Sensors 2024, 24(2), 657; https://doi.org/10.3390/s24020657 - 19 Jan 2024

Viewed by 2352

Abstract

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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16 pages, 4267 KiB

Open AccessArticle

Integrated Quantitative Evaluation of Spatial Cognition and Motor Function with HoloLens Mixed Reality

by Kenya Tada, Yuhei Sorimachi, Kyo Kutsuzawa, Dai Owaki and Mitsuhiro Hayashibe

Sensors 2024, 24(2), 528; https://doi.org/10.3390/s24020528 - 15 Jan 2024

Cited by 7 | Viewed by 1657

Abstract

The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has attracted much attention in recent years. MR displays virtual objects on a head-mounted see-through display that overlies the user’s field of vision and allows users to manipulate them as if they exist in reality. However, tasks in previous studies applying MR to rehabilitation have been limited to tasks in which the virtual objects are static and do not interact dynamically with the surrounding environment. Therefore, in this study, we developed an application to evaluate cognitive and motor functions with the aim of realizing a rehabilitation system that is dynamic and has interaction with the surrounding environment using MR technology. The developed application enabled effective evaluation of the user’s spatial cognitive ability, task skillfulness, motor function, and decision-making ability. The results indicate the usefulness and feasibility of MR technology to quantify motor function and spatial cognition both for static and dynamic tasks in rehabilitation. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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14 pages, 1935 KiB

Open AccessArticle

The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects

by Aliaa Gouda and Jan Andrysek

Sensors 2024, 24(2), 400; https://doi.org/10.3390/s24020400 - 9 Jan 2024

Cited by 4 | Viewed by 1661

Abstract

Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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18 pages, 4061 KiB

Open AccessArticle

Concurrent Validity of Cervical Movement Tests Using VR Technology—Taking the Lab to the Clinic

by Karin Forsberg, Johan Jirlén, Inger Jacobson and Ulrik Röijezon

Sensors 2023, 23(24), 9864; https://doi.org/10.3390/s23249864 - 16 Dec 2023

Cited by 1 | Viewed by 1722

Abstract

Reduced cervical range of motion (ROM) and movement velocity are often seen in people with neck pain. Objective assessment of movement characteristics is important to identify dysfunction, to inform tailored interventions, and for the evaluation of the treatment effect. The purpose of this study was to investigate the concurrent validity of a newly developed VR technology for the assessment of cervical ROM and movement velocity. VR technology was compared against a gold-standard three-dimensional optical motion capture system. Consequently, 20 people, 13 without and 7 with neck pain, participated in this quantitative cross-sectional study. ROM was assessed according to right/left rotation, flexion, extension, right/left lateral flexion, and four diagonal directions. Velocity was assessed according to fast cervical rotation to the right and left. The correlations between VR and the optical system for cervical ROM and velocity were excellent, with intraclass correlation coefficient (ICC) values > 0.95. The mean biases between VR and the optical system were ≤ 2.1° for the ROM variables, <12°/s for maximum velocity, and ≤3.0°/s for mean velocity. In conclusion, VR is a useful assessment device for ROM and velocity measurements with clinically acceptable biases. It is a feasible tool for the objective measurement of cervical kinematics in the clinic. Full article

(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)

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