# AI-driven low-cost rehabilitation exergame as a lightweight framework for stroke assessment

## TL;DR

A low-cost, AI-powered exergame is developed to assess upper-limb motor function in stroke patients using a standard camera, offering a scalable and accessible alternative to traditional clinical assessments.

## Contribution

The novel contribution is a sensor-free, AI-driven exergame that automatically estimates motor performance and severity during gameplay, outperforming complex models with a lightweight approach.

## Key findings

- Kinematic and spatiotemporal features extracted from gameplay correlated strongly with FMA scores.
- A linear regression model achieved high predictive accuracy (Spearman ρ = 0.92, R² = 0.89) and severity classification accuracy of 86–93%.
- The framework is sensor-free, scalable, and provides immediate feedback for telerehabilitation and remote monitoring.

## Abstract

Stroke is a leading cause of long-term disability, often affecting upper-limb motor function and requiring continuous assessment. The Fugl-Meyer Assessment (FMA), though a clinical gold standard, is time-consuming and demands specialized personnel. This study presents an AI-driven, low-cost rehabilitation exergame that simultaneously provides therapy and automatically estimates upper-limb motor performance during gameplay using only a standard camera. Sixteen kinematic and spatiotemporal features were extracted from 2D hand and arm trajectories of twelve post-stroke individuals (24 limbs, 14 affected) using the MediaPipe framework. Features such as hand angle, range of motion, movement area, traveled distance, and shoulder–elbow coordination showed strong correlations with FMA scores and stratified participants by motor severity. A lightweight linear regression model achieved high predictive performance (Spearman ρ = 0.92, R² = 0.89, RMSE = 4.42) and classified severity levels with 86–93% accuracy. This interpretable approach outperformed complex machine learning models, highlighting the clinical relevance of transparent metrics embedded in gameplay. The proposed framework is sensor-free, scalable, and reproducible, offering immediate feedback while reducing clinical workload and enabling accessible digital biomarkers for telerehabilitation and remote monitoring after stroke.