A Pilot Study of Applying Machine Learning to Adjust the Content Generation and Personalization in Developing a Virtual Reality Hand Grip Strength Exergame Prototype

• Published in: 2023 Sixth International Symposium on Computer, Consumer and Control (IS3C) pp. 107 - 109
• Main Authors: Chen, Pai-Hsun, Wang, Yin-Nan, Chen, Lu-Han
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2023
• Subjects: Exergame; Games; Machine Learning; Machine learning algorithms; Procedural Content Generation; Prototypes; Reinforcement learning; Three-dimensional displays; Training; Virtual reality
• ISSN: 2770-0496
• DOI: 10.1109/IS3C57901.2023.00037

This paper presents a prototype of a virtual reality exercise game that uses machine learning to control content generation and game personalization. The game aims to provide a personalized workout experience for users by generating content that is tailored to their individual grip training level, interests and preferences. Genetic algorithms and artificial intelligence neural network algorithms are used to analyze user data such as their biometrics, workout history and feedback to generate challenging but achievable personalized workout routines. The game also incorporates gamification designs to promote engagement and motivation, such as NPC, score, rewards and so on. The prototype was evaluated through user research, which showed that participants found the content motivating and enjoyable. The results suggest that using machine learning for content generation and personalization can improve the user experience and encourage adherence to the training application in a virtual reality environment.