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

 

'Assessments', biomarkers and predictions don't get you recovered, only EXACT PROTOCOLS DO! SURVIVORS WANT RECOVERY! GET THERE!

I'd fire everyone involved with this crapola! You're predicting based on the failure of the status quo! Change the status quo, you blithering idiots!

The only possibility of assessments being useful is if they POINT DIRECTLY TO EXACT REHAB PROTOCOLS! This did nothing towards that so completely fucking useless!

'Assessments' like Fugl-Meyer NEVER GET ANYONE RECOVERED! I'd have you all fired for incompetency in not solving stroke!

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

• Júlia Tannús, 
• Caroline Valentini & 
• Eduardo Naves 

npj Digital Medicine , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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.

Data availability

The datasets generated and analyzed during the current study are not publicly available at this stage but are available from the corresponding author (J.T.) upon reasonable request. We also intend to make the anonymized dataset publicly accessible through an open research repository following publication. The custom Python scripts used for data preprocessing, feature extraction, and regression analysis are available from the corresponding author upon request.

References

1. Tsao, C. W. et al. Heart Disease and Stroke Statistics-2022 Update: a report from the american heart association. Circulation 145, e153–e639 (2022).