Generative AI Based Difficulty Level Design of Serious Games for Stroke Rehabilitation

Abstract

Internet-of-Things (IoT) based solutions are gaining momentum in delivering efficient solutions in health care domain, reducing financial and physical burden on patients and improving ease of treatment for physicians. One such smart health care solution are Serious games. Serious games aid rehabilitation in various fields. For physical rehabilitation, personalization is important for improving training results. A scientific approach for difficulty level design can facilitate players to get effective rehabilitation. The automation of personalized difficulty level design helps the self-guided game-based rehabilitation approach, become simplified and efficient. AI is advancing the design of personalized serious game for rehabilitation through data-driven and individual-oriented methods. In this work, we present Generative AI based design of gamified training plan, especially difficulty level plan which could go beyond rule based solutions. We apply Generative Adversarial Networks (GANs) to address the problem arising from large sequential data and variable requirement. This helps to overcome the limitation of unrealistic long term practice session for a rehabilitation patient by simplifying the training time. When compared with the results from Long Short-Term Memory (LSTM) based approach, our GANs based approach gave a 4.5X less variation in difficulty level and 6.5X less loss which proved the efficacy of our proposed approach in generating accurate difficulty levels. When compared with existing literature our proposed work simultaneously performs better on various parameters namely faster convergence, minimum emphasis on past performance of players, low data requirement for training and demographic flexibility.