JMIR Publications

JMIR Rehabilitation and Assistive Technologies

Development and Evaluation of Rehabilitation, Physiotherapy and Assistive Technologies, Robotics, Prosthetics and Implants, Mobility and Communication Tools, Home Automation and Telerehabilitation


Recent Articles:

  • App in use (montage). Source: The Authors /; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation


    Background: Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. Objective: We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. Methods: A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. Results: The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. Conclusions: A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU.

  • Diagram depicting increasing specificity of activity recognition models in terms of what groups of individuals (able-bodied or individuals with disabilities/patients) they are trained on. Patients are depicted using their control (black) or novel (red) assistive device. Each model is used to predict activities for the patient of interest (Test), walking with the novel assistive device. The top 3 layers of the pyramid contain global models, which are trained on individuals other than the one used to test the model. The 2 bottom layers of the pyramid contain personal models, which are trained and tested with data from the same individual. Source: Image created by the authors; Copyright: The authors; URL:; License: Creative Commons Attribution (CC-BY).

    Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models


    Background: Wearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition. Objective: The objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals. Methods: Data were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject. Results: Median accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition. Conclusions: Our results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations.

  • Telemedicine consult. Source: Flickr; Copyright: Intel Free Press; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Telerehabilitation: Review of the State-of-the-Art and Areas of Application


    Background: Telemedicine applications have been increasing due to the development of new computer science technologies and of more advanced telemedical devices. Various types of telerehabilitation treatments and their relative intensities and duration have been reported. Objective: The objective of this review is to provide a detailed overview of the rehabilitation techniques for remote sites (telerehabilitation) and their fields of application, with analysis of the benefits and the drawbacks related to use. We discuss future applications of telerehabilitation techniques with an emphasis on the development of high-tech devices, and on which new tools and applications can be used in the future. Methods: We retrieved relevant information and data on telerehabilitation from books, articles and online materials using the Medical Subject Headings (MeSH) “telerehabilitation,” “telemedicine,” and “rehabilitation,” as well as “disabling pathologies.” Results: Telerehabilitation can be considered as a branch of telemedicine. Although this field is considerably new, its use has rapidly grown in developed countries. In general, telerehabilitation reduces the costs of both health care providers and patients compared with traditional inpatient or person-to-person rehabilitation. Furthermore, patients who live in remote places, where traditional rehabilitation services may not be easily accessible, can benefit from this technology. However, certain disadvantages of telerehabilitation, including skepticism on the part of patients due to remote interaction with their physicians or rehabilitators, should not be underestimated. Conclusions: This review evaluated different application fields of telerehabilitation, highlighting its benefits and drawbacks. This study may be a starting point for improving approaches and devices for telerehabilitation. In this context, patients’ feedback may be important to adapt rehabilitation techniques and approaches to their needs, which would subsequently help to improve the quality of rehabilitation in the future. The need for proper training and education of people involved in this new and emerging form of intervention for more effective treatment can’t be overstated.

  • Training mode app. Source: Image created by the authors; Copyright: The authors; URL:; License: Creative Commons Attribution (CC-BY).

    Mobile Phone–Supported Physiotherapy for Frozen Shoulder: Feasibility Assessment Based on a Usability Study


    Background: Patients with frozen shoulder show limited shoulder mobility often accompanied by pain. Common treatment methods include physiotherapy, pain medication, administration of corticosteroids, and surgical capsulotomy. Frozen shoulder often lasts from months to years and mostly affects persons in the age group of 40 to 70 years. It severely reduces the quality of life and the ability to work. Objective: The objective of this study was to evaluate the feasibility of a mobile health (mHealth) intervention that supports patients affected by “stage two” frozen shoulder. Patients were supported with app-based exercise instructions and tools to monitor their training compliance and progress. These training compliance and progress data supplement the patients’ oral reports to the physiotherapists and physicians and can assist them in therapy adjustment. Methods: In order to assess the feasibility of the mHealth intervention, a pilot study of a newly developed app for frozen shoulder patients was conducted with 5 patients for 3 weeks. The main function of the app was the instruction for exercising at home. Standardized questionnaires on usability such as System Usability Scale (SUS) and USE (Usefulness, Satisfaction, and Ease of use), and Technology Acceptance Model-2 (TAM-2) were completed by the study participants at the end of the study. Additionally, a nonstandardized questionnaire was completed by all patients. The correctness of the exercises as conducted by the patients was assessed by a physiotherapist at the end of the study. The mobility of the shoulder and pain in shoulder movement was assessed by a physiotherapist at the start and the end of the study. Results: The pilot study was successfully conducted, and the app was evaluated by the patients after 3 weeks. The results of the standardized questionnaires showed high acceptance (TAM-2) and high usability (SUS) of the developed app. The overall usability of the system as assessed by the SUS questionnaire was very good (an average score of 88 out of 100). The average score of the TAM-2 questionnaire on the intention to further use the app was 4.2 out of 5, which indicated that most patients would use the app if further available. The results of the USE questionnaires highlighted that the patients learned how to use the app easily (an average score of 4.2 out of 5) and were satisfied with the app (an average score of 4.7 out of 5). The frequency of app usage and training was very high based on patient reports and verified by analysis of the usage data. The patients conducted the exercises almost flawlessly. Conclusions: Our results indicate the feasibility of the mHealth intervention, as the app was easy to use and frequently used by the patients. The app supported the patients’ physiotherapy by providing clear exercising instructions.

  • Detect grasping action using force myographic signal. Source: Image created by the authors; Copyright: The authors; URL:; License: Creative Commons Attribution (CC-BY).

    Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals


    Background: Functional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated. In contrast to previous studies, this exploratory study investigates the feasibility of (1) detecting grasping when the participants move their arms, which could introduce large artifacts to the point of potentially preventing the practical use of the proposed technology, and (2) counting grasping during arm-reaching tasks. Objective: The aim of this study was to determine the usefulness of FMG in the detection of functional arm movements. The use of FMG straps placed on the forearm is proposed for counting the number of grasping actions in the presence of arm movements. Methods: Ten healthy volunteers participated in this study to perform a pick-and-place exercise after providing informed consent. FMG signals were simultaneously collected using 2 FMG straps worn on their wrist and at the midposition of their forearm, respectively. Raw FMG signals and 3 additional FMG features (ie, root mean square, wavelength, and window symmetry) were extracted and fed into a linear discriminant analysis classifier to predict grasping states. The transition from nongrasping to grasping states was detected during the process of counting the number of grasping actions. Results: The median accuracy for detecting grasping events using FMG recorded from the wrist was 95%, and the corresponding interquartile range (IQR) was 5%. For forearm FMG classification, the median accuracy was 92%, and the corresponding IQR was 3%. The difference between the 2 median accuracies was statistically significant (P<.001) when using a paired 2-tailed sign test. The median percentage error for counting grasping events when FMG was recorded from the wrist was 1%, and the corresponding IQR was 2%. The median percentage error for FMG recorded from the forearm was 2%, and the corresponding IQR was also 2%. While the median percentage error for the wrist was lower than that of the forearm, the difference between the 2 was not statistically significant based on a paired 2-tailed sign test (P=.29). Conclusions: This study reports that grasping can reliably be counted using an unobtrusive and simple FMG strap even in the presence of arm movements. Such a result supports the foundation for future research evaluating the feasibility of monitoring hand grasping during unsupervised ADL, leading to further investigations with individuals with motor function deficits of the arm.

  • The Hinge Health kit: a tablet computer preloaded with the Hinge Health software, and two custom-made movement sensors to guide participants through their exercises. Source: Image created by the authors.; Copyright: The authors; URL:; License: Creative Commons Attribution + Noncommercial + ShareAlike (CC-BY-NC-SA).

    Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program


    Background: Chronic knee pain (CKP) affects a large number of adults, many of whom do not receive best-practice care and are at high risk for unnecessary surgery. Objective: The aim of this study was to investigate the effect of the Hinge Health 12-week digital care program (DCP) for CKP on knee pain and function, with secondary outcomes of surgery interest and satisfaction, at 12 weeks and 6 months after starting the program. Methods: Individuals with CKP were recruited onto the 12-week program, comprising sensor-guided physical exercises, weekly education, activity tracking, and psychosocial support such as personal coaching and cognitive behavioral therapy (CBT). We used a single-arm design with assessment of outcomes at baseline, 12 weeks, and 6 months after starting the program. We used a linear mixed effects model with Tukey contrasts to compare timepoints and report intention-to-treat statistics with last observation carried forward. Results: The cohort consisted of 41 individuals (32 female, mean age 52 years, SD 9 years). Between baseline and week 12, participants reported clinically significant improvements in the Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) function scales of 16 points (95% CI 12-21, P<.001) and 10 points (95% CI 6-14, P<.001), respectively. Significant reductions of 57% (mean difference 30, 95% CI 21-38, P<.001) and 51% (mean difference 25, 95% CI 16-33, P<.001) in visual analog scale (VAS) knee pain and stiffness, respectively, were observed at 12 weeks, as well as a 67% reduction in surgery interest (mean reduction 2.3 out of 10, 95% CI 1.5-3.1, P<.001). Average satisfaction at week 12 was 9.2 out of 10. Critically, all improvements were maintained at 6 months at similar or greater magnitude. Conclusions: Participants on the Hinge Health DCP for CKP showed substantial clinical improvements that were maintained 6 months after enrolling in the program. This shows that DCPs carry strong potential to deliver evidence-based, cost-effective care to those suffering from CKP.

  • Mobili-T(R) system and past iterations. Copyright: Image created by the authors;

    Designing a Mobile Health App for Patients With Dysphagia Following Head and Neck Cancer: A Qualitative Study


    Background: Adherence to swallowing rehabilitation exercises is important to develop and maintain functional improvement, yet more than half of head and neck cancer (HNC) patients report having difficulty adhering to prescribed regimens. Health apps with game elements have been used in other health domains to motivate and engage patients. Understanding the factors that impact adherence may allow for more effective gamified solutions. Objective: The aim of our study was to (1) identify self-reported factors that influence adherence to conventional home therapy without a mobile device in HNC patients and (2) identify appealing biofeedback designs that could be used in a health app. Methods: A total of 10 (4 females) HNC patients (mean=60.1 years) with experience completing home-based rehabilitation programs were recruited. Thematic analysis of semi-structured interviews was used to answer the first objective. Convergent interviews were used to obtain reactions to biofeedback designs. Results: Facilitators and barriers of adherence to home therapy were described through 6 themes: patient perceptions on outcomes and progress, clinical appointments, cancer treatment, rehabilitation program, personal factors, and connection. App visuals that provide feedback on performance during swallowing exercises should offer an immediate representation of effort relative to a goal. Simple, intuitive graphics were preferred over complex, abstract ones. Continued engagement with the app could be facilitated by tracking progress and by using visuals that build structures with each use. Conclusions: This is a detailed documentation of the initial steps in designing a health app for a specific patient group. Results revealed the importance of patient engagement in early stages of app development.

  • Mobile mirror therapy facilitated by augmented reality using the tablet-integrated camera. Source: Figure 5 from; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Design and Development of a Telerehabilitation Platform for Patients With Phantom Limb Pain: A User-Centered Approach


    Background: Phantom limb pain is a frequent and persistent problem following amputation. Achieving sustainable favorable effects on phantom limb pain requires therapeutic interventions such as mirror therapy that target maladaptive neuroplastic changes in the central nervous system. Unfortunately, patients’ adherence to unsupervised exercises is generally poor and there is a need for effective strategies such as telerehabilitation to support long-term self-management of patients with phantom limb pain. Objective: The main aim of this study was to describe the user-centered approach that guided the design and development of a telerehabilitation platform for patients with phantom limb pain. We addressed 3 research questions: (1) Which requirements are defined by patients and therapists for the content and functions of a telerehabilitation platform and how can these requirements be prioritized to develop a first prototype of the platform? (2) How can the user interface of the telerehabilitation platform be designed so as to match the predefined critical user requirements and how can this interface be translated into a medium-fidelity prototype of the platform? (3) How do patients with phantom limb pain and their treating therapists judge the usability of the medium-fidelity prototype of the telerehabilitation platform in routine care and how can the platform be redesigned based on their feedback to achieve a high-fidelity prototype? Methods: The telerehabilitation platform was developed using an iterative user-centered design process. In the first phase, a questionnaire followed by a semistructured interview was used to identify the user requirements of both the patients and their physical and occupational therapists, which were then prioritized using a decision matrix. The second phase involved designing the interface of the telerehabilitation platform using design sketches, wireframes, and interface mock-ups to develop a low-fidelity prototype. Heuristic evaluation resulted in a medium-fidelity prototype whose usability was tested in routine care in the final phase, leading to the development of a high-fidelity prototype. Results: A total of 7 categories of patient requirements were identified: monitoring, exercise programs, communication, settings, background information, log-in, and general requirements. One additional category emerged for therapists: patient management. Based on these requirements, patient and therapist interfaces for the telerehabilitation platform were developed and redesigned by the software development team in an iterative process, addressing the usability problems that were reported by the users during 4 weeks of field testing in routine care. Conclusions: Our findings underline the importance of involving the users and other stakeholders early and continuously in an iterative design process, as well as the need for clear criteria to identify critical user requirements. A decision matrix is presented that incorporates the views of various stakeholders in systematically rating and prioritizing user requirements. The findings and lessons learned might help health care providers, researchers, software designers, and other stakeholders in designing and evaluating new teletreatments, and hopefully increase the likelihood of user acceptance.

  • Old man's phone. Image Source: Author: Jcfrog. Copyright: CC0 Public Domain.

    Technologies to Support Community-Dwelling Persons With Dementia: A Position Paper on Issues Regarding Development, Usability, Effectiveness and...


    Background: With the expected increase in the numbers of persons with dementia, providing timely, adequate, and affordable care and support is challenging. Assistive and health technologies may be a valuable contribution in dementia care, but new challenges may emerge. Objective: The aim of our study was to review the state of the art of technologies for persons with dementia regarding issues on development, usability, effectiveness and cost-effectiveness, deployment, and ethics in 3 fields of application of technologies: (1) support with managing everyday life, (2) support with participating in pleasurable and meaningful activities, and (3) support with dementia health and social care provision. The study also aimed to identify gaps in the evidence and challenges for future research. Methods: Reviews of literature and expert opinions were used in our study. Literature searches were conducted on usability, effectiveness and cost-effectiveness, and ethics using PubMed, Embase, CINAHL, and PsycINFO databases with no time limit. Selection criteria in our selected technology fields were reviews in English for community-dwelling persons with dementia. Regarding deployment issues, searches were done in Health Technology Assessment databases. Results: According to our results, persons with dementia want to be included in the development of technologies; there is little research on the usability of assistive technologies; various benefits are reported but are mainly based on low-quality studies; barriers to deployment of technologies in dementia care were identified, and ethical issues were raised by researchers but often not studied. Many challenges remain such as including the target group more often in development, performing more high-quality studies on usability and effectiveness and cost-effectiveness, creating and having access to high-quality datasets on existing technologies to enable adequate deployment of technologies in dementia care, and ensuring that ethical issues are considered an important topic for researchers to include in their evaluation of assistive technologies. Conclusions: Based on these findings, various actions are recommended for development, usability, effectiveness and cost-effectiveness, deployment, and ethics of assistive and health technologies across Europe. These include avoiding replication of technology development that is unhelpful or ineffective and focusing on how technologies succeed in addressing individual needs of persons with dementia. Furthermore, it is suggested to include these recommendations in national and international calls for funding and assistive technology research programs. Finally, practitioners, policy makers, care insurers, and care providers should work together with technology enterprises and researchers to prepare strategies for the implementation of assistive technologies in different care settings. This may help future generations of persons with dementia to utilize available and affordable technologies and, ultimately, to benefit from them.

  • Hearing screening training using tablet based audiometry in Guatemala. Image sourced and copyright owned by authors.

    Validated Smartphone-Based Apps for Ear and Hearing Assessments: A Review


    Background: An estimated 360 million people have a disabling hearing impairment globally, the vast majority of whom live in low- and middle-income countries (LMICs). Early identification through screening is important to negate the negative effects of untreated hearing impairment. Substantial barriers exist in screening for hearing impairment in LMICs, such as the requirement for skilled hearing health care professionals and prohibitively expensive specialist equipment to measure hearing. These challenges may be overcome through utilization of increasingly available smartphone app technologies for ear and hearing assessments that are easy to use by unskilled professionals. Objective: Our objective was to identify and compare available apps for ear and hearing assessments and consider the incorporation of such apps into hearing screening programs Methods: In July 2015, the commercial app stores Google Play and Apple App Store were searched to identify apps for ear and hearing assessments. Thereafter, six databases (EMBASE, MEDLINE, Global Health, Web of Science, CINAHL, and mHealth Evidence) were searched to assess which of the apps identified in the commercial review had been validated against gold standard measures. A comparison was made between validated apps. Results: App store search queries returned 30 apps that could be used for ear and hearing assessments, the majority of which are for performing audiometry. The literature search identified 11 eligible validity studies that examined 6 different apps. uHear, an app for self-administered audiometry, was validated in the highest number of peer reviewed studies against gold standard pure tone audiometry (n=5). However, the accuracy of uHear varied across these studies. Conclusions: Very few of the available apps have been validated in peer-reviewed studies. Of the apps that have been validated, further independent research is required to fully understand their accuracy at detecting ear and hearing conditions.

  • Physiotherapy. Image source: Author: SEppl. Copyright: Public Domain.

    Internet-Based Exercise Therapy Using Algorithms for Conservative Treatment of Anterior Knee Pain: A Pragmatic Randomized Controlled Trial


    Background: Conservative treatment remains the first-line option, and there is significant medical evidence showing that home-based exercise therapy for the treatment of common causes of knee pain is effective. SimpleTherapy created an online platform that delivers Internet-based exercise therapy for common causes of knee pain. The system is driven by an algorithm that can process the user’s feedback to provide an adaptive exercise regimen. This triple-armed, pragmatic randomized pilot was designed to evaluate if this telerehabilitation platform is safe and effective. Objective: We hypothesized that a home-based, algorithm-driven exercise therapy program can be safe for use and even improve compliance over the standard of care, the paper handout. Methods: After an independent internal review board review and approval, the website was opened. Once the trial was open for enrollment, no changes to the functionality or user interaction features were performed until the trial had closed. User accrual to the website was done using website optimization and social media postings tied to existence of knee pain. Consent was obtained online through checkboxes with third-party signature confirmation. No fees were charged to any patient. Patients were recruited online from an open access website. Outcomes were self-assessed through questionnaires with no face-to-face clinician interaction. A triple-arm randomized controlled trial was used with arm 1 being a static handout of exercises, arm 2 being a video version of arm 1, and arm 3 being a video-based, algorithm-driven system that took patient feedback and changed the exercises based on the feedback. Patients used household items and were not supervised by a physical therapist or clinician. Patients were reminded at 48-hour intervals to complete an exercise session. Results: A total of 860 users found the trial and initiated the registration process. These 860 were randomized, and the demographic distribution shows the randomization was successful. In all, 70 users completed the 6-week regimen (8.1%): 20 users were in arm 1, 33 users in arm 2, and 17 users in arm 3. There were no adverse events reported in any of the 3 arms. All outcomes were self-assessed. No adverse events were reported during or after the trial. Conclusions: Because only 8.1% of those who enrolled completed the trial, an intent-to-treat analysis did not reach statistical significance in this pilot trial. However, the completion rates are comparable to those of previous online-only trials. Given an early phase trial, no adverse events were reported. Ongoing data collection continues and will form the basis for further data on the efficacy of this intervention. Trial Registration: NCT01696162; (Archived by WebCite at

  • Heel strike feedback screen for stroke survivors and insole technology. Image created and copyright owned by authors.

    A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole


    Background: In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges. Objective: Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home. Methods: A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a “smart insole” to facilitate measurement of walking activities in a free-living, nonrestrictive environment. Results: Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants’ affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use. Conclusion: Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants.

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