ReHAb Playground: A DL-Based Framework for Game-Based Hand Rehabilitation

Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built in the Unity Game Engine. The system utilizes a YOLOv10n model for hand gesture classification and MediaPipe Hands for 3D hand landmark extraction. Three mini-games were developed to target specific motor and cognitive functions: Cube Grab, Coin Collection, and Simon Says. Key gameplay parameters, namely repetitions, time limits, and gestures, can be tuned according to therapeutic protocols. Experiments with healthy participants were conducted to establish reference performance ranges based on average completion times and standard deviations. The results showed a consistent decrease in both task completion and gesture times across trials, indicating learning effects and improved control of gesture-based interactions. The most pronounced improvement was observed in the more complex Coin Collection task, confirming the system’s ability to support skill acquisition and engagement in rehabilitation-oriented activities. ReHAb Playground was conceived with modularity and scalability at its core, enabling the seamless integration of additional exercises, gesture libraries, and adaptive difficulty mechanisms. While preliminary, the findings highlight its promise as an accessible, low-cost rehabilitation platform suitable for home use, capable of monitoring motor progress over time and enhancing patient adherence through engaging, game-based interactions. Future developments will focus on clinical validation with patient populations and the implementation of adaptive feedback strategies to further personalize the rehabilitation process.