An Investigation of Hand Gestures for Controlling Video Games in a Rehabilitation Exergame System

Musculoskeletal disorders (MSDs) can significantly impact individuals' quality of life (QoL), often requiring effective rehabilitation strategies to promote recovery. However, traditional rehabilitation methods can be expensive and may lack engagement, leading to poor adherence to therapy exercise routines. An exergame system can be a solution to this problem. In this paper, we investigate appropriate hand gestures for controlling video games in a rehabilitation exergame system. The Mediapipe Python library is adopted for the real-time recognition of gestures. We choose 10 easy gestures among 32 possible simple gestures. Then, we specify and compare the best and the second-best groups used to control the game. Comprehensive experiments are conducted with 16 students at Andalas University, Indonesia, to find appropriate gestures and evaluate user experiences of the system using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The results show that the hand gestures in the best group are more accessible than in the second-best group. The results suggest appropriate hand gestures for game controls and confirm the proposal's validity. In future work, we plan to enhance the exergame system by integrating a diverse set of video games, while expanding its application to a broader and more diverse sample. We will also study other practical applications of the hand gesture control function.