Effectiveness of a telerehabilitation program using wearable sensors for post-stroke upper-limb motor recovery: a randomized controlled trial

Project Abstract

This randomized controlled trial evaluated the effectiveness of a home-based telerehabilitation program incorporating wearable sensors to enhance upper-limb motor recovery after stroke. A total of 120 adults (aged 40–75) with subacute to chronic stroke (1–12 months post-event) were recruited from three rehabilitation centers and randomized to either the telerehabilitation intervention or conventional in-clinic therapy over an 8-week period, with a 12-week follow-up. The intervention combined real-time remote coaching, task-specific upper-limb exercises, and wireless sensor feedback to monitor movement quality and adherence. Wearable inertial measurement units captured kinematic metrics including range of motion, movement smoothness, speed, and symmetry, transmitted to a cloud-based dashboard accessible to clinicians and participants. The control group received standard care consisting of in-person sessions matched for frequency and duration. Primary outcomes included the Fugl-Meyer Assessment for the upper extremity (FMA-UE) and the Box and Block Test (BBT) to measure motor impairment and gross dexterity, respectively. Secondary outcomes encompassed the Motor Activity Log (MAL), Stroke Impact Scale (SIS-3.0), grip strength, and adherence metrics derived from wearable data (daily active time, repetitions, and error rates). Assessments occurred at baseline, post-intervention (8 weeks), and follow-up (12 weeks). Blinded assessors conducted all evaluations. Intention-to-treat analyses were conducted using mixed-effects models to account for repeated measures and potential confounders such as age, time since stroke, and baseline impairment. Results demonstrated that the telerehabilitation group achieved significantly greater improvements in FMA-UE scores at 8 weeks compared with controls (mean difference 6.4 points; 95% CI 3.2–9.6; p<0.001), with sustained gains at 12 weeks (mean difference 5.1 points; 95% CI 2.0–8.2; p=0.002). BBT performance improved more in the intervention group (mean difference 4.2 blocks; 95% CI 1.5–6.9; p=0.003). MAL scores reflected enhanced perceived daily use of the affected arm (mean difference 1.1 points on the log-scale; 95% CI 0.4–1.8; p=0.004). SIS-3.0 domains indicated better perceived physical function and participation in the telerehabilitation group. Wearable-derived adherence correlated positively with motor gains (r=0.42, p=0.01), and higher movement repetition counts were associated with greater FMA-UE improvements (p<0.01). No serious adverse events related to the intervention were reported. Subgroup analyses suggested that participants with moderate impairment benefited most, though individuals with severe impairment still demonstrated clinically meaningful gains. The study supports telerehabilitation with wearable-sensor feedback as an effective, scalable approach to improving upper-limb motor recovery post-stroke. The integration of objective kinematic data with remote coaching enhanced adherence and allowed for individualized progression, suggesting potential for broader adoption in community and rural settings. Limitations include potential selection bias toward motivated participants and the need for reliable internet access, which may influence generalizability. Future work should explore long-term outcomes, cost-effectiveness, and optimization of sensor-driven feedback to maximize functional recovery across diverse stroke populations.

Project Overview

What This Project Is About

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A straightforward look at using remote rehabilitation with wearable devices to help people recover arm movement after a stroke. The project tests whether guiding therapy remotely with sensors can improve arm function compared with usual care.

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The Problem It Addresses

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Objectives of the Project

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1. Assess whether telerehabilitation with wearables improves upper-limb motor scores more than standard care.
2. Evaluate patient adherence to the home program and its relationship to outcomes.
3. Explore user satisfaction and acceptability of the wearable tech and remote coaching.
4. Identify any barriers or facilitators to implementing telerehabilitation in clinics.

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What You Will Do Step by Step

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1) Review literature on post-stroke rehabilitation and telerehab with wearables.

2) Recruit participants who have had a stroke and meet eligibility criteria.

3) Randomly assign to telerehabilitation with wearables or standard care.

4) Collect baseline data on arm strength and function.

5) Deliver remote therapy sessions monitored by wearables over several weeks.

6) Collect follow-up data at multiple time points and analyze changes.

7) Compare groups using basic statistics to determine effectiveness.

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Expected Outcome

A clearer picture of whether wearable-guided telerehabilitation can boost arm recovery after stroke, with insights into feasibility, patient acceptance, and potential for wider use in clinics.