Development and evaluation of an AI-powered wearable system for objective assessment and training of upper-limb motor function in post-stroke rehabilitation

Project Abstract

This study presents the development and comprehensive evaluation of an AI-powered wearable system designed to objectively assess and train upper-limb motor function in individuals recovering from stroke. The system integrates a lightweight, multi-sensor wearable glove and forearm band embedded with inertial measurement units (IMUs), surface electromyography (sEMG) sensors, and force sensors to capture rich kinematic, kinetic, and muscular activation data in real-world and clinical settings. An AI-driven analytics pipeline processes these multimodal signals to quantify motor impairments, track longitudinal recovery, and personalize rehabilitation protocols. We implemented supervised and unsupervised machine learning models to classify movement quality, detect compensatory strategies, and predict functional outcomes such as reach-to-grasp accuracy, grip strength, and activities of daily living (ADL) independence. The system generates real-time feedback including motion coaching, objective scores, and adaptive exercise recommendations, enabling concurrent therapy and remote monitoring by clinicians. Methodologically, we conducted three iterative phases (i) hardware and software integration with ergonomic design trials to ensure user comfort and data fidelity across varied post-stroke presentations; (ii) algorithm development and validation using a dataset collected from 120 participants (60 post-stroke and 60 age-matched controls) performing a standardized upper-limb assessment battery and task-specific rehabilitation exercises; and (iii) a 12-week randomized controlled pilot trial (n=40 stroke survivors) comparing the AI-powered wearable regimen against conventional therapy. Outcome measures encompassed objective metrics such as movement smoothness (jerk and spectral entropy), kinematic markers (range of motion, velocity profiles), synergy patterns (muscle coordination indices), as well as functional scales (Fugl-Meyer Assessment—Upper Extremity, Box and Block Test, and Motor Activity Log). User experience was evaluated through acceptability, perceived workload, and adherence rates. Key findings indicate that the wearable system achieves high fidelity in gesture recognition (precision > 92%, recall > 90%), robust detection of compensatory strategies with 85% accuracy, and predictive capability for 6-month functional independence with an AUC of 0.88 in cross-validated models. The 12-week intervention group demonstrated superior gains in Fugl-Meyer scores (mean improvement 9.5 points vs 4.1 in controls, p<0.01), greater reach and grip strength improvements, and higher adherence due to engaging feedback loops and remotely monitored progress. Clinician usability assessments showed efficient integration into rehabilitation workflows and meaningful reductions in assessment time. Adverse events were rare and limited to transient discomfort from sensor placement. The study confirms that AI-powered wearables can provide objective, sensitive, and scalable assessment of upper-limb impairment while delivering personalized, motivating, and effective rehabilitation. implications include potential for remote, clinic-to-home pathways, data-driven personalization of therapy intensities, and enhanced translational impact for stroke rehabilitation paradigms. Future work will expand sensor modalities, explore transfer learning across patient cohorts, and integrate vascular and cognitive considerations to further optimize outcomes.

Project Overview

What This Project Is About

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A straightforward, beginner-friendly look at using a wearable device and AI to measure and improve arm movement after a stroke. The project combines simple sensors, computer software, and clear feedback to help people regain upper-limb function.

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The Problem It Addresses

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Many stroke patients have limited arm movement and limited access to regular therapy. Traditional methods can be subjective and hard to monitor over time. This project aims to provide objective measurements and guided practice at home or in clinics.

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Objectives of the Project

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1. Develop a wearable system that collects movement data from the upper limb.
2. Create simple software that analyzes the data to quantify motor function.
3. Incorporate AI to recognize patterns and tailor training tasks.
4. Test usability with healthy volunteers before small patient trials.
5. Evaluate improvements in movement precision and range of motion.

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What You Will Do Step by Step

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1) Learn basics of wearable sensors and data collection. 2) Build a basic wearable prototype and connect it to a computer. 3) Collect movement data during repetitive arm tasks. 4) Develop simple algorithms to measure speed, smoothness, and range. 5) Train an AI model to customize exercises. 6) Run user tests to gather feedback on ease of use. 7) Analyze data to see preliminary gains. 8) Document results and propose improvements.

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Expected Outcome

We expect a usable wearable-and-software package that provides objective limb-function scores and personalized training suggestions, along with initial evidence that users show improved arm control after training.