Enhancing Gamified Training Usability for Prosthesis via Reward Based Learning AI Model

Peter Smith, Matt Dombrowski, Viviana Rivera, Maanya Pradeep, Eric Gass, John Sparkman and Albert Manero

Simulation & Gaming, 2025, vol. 56, issue 3, 326-343

Abstract: Background Pediatric electromyographic prostheses have limited accessibility due to factors including device weight, training challenges, and aesthetics. When prescribed, high rejection rates still remain. In an effort to reduce rejection, a video game training platform was developed to improve training outcomes. Utilizing this platform shifts training to a low stress virtual environment and can include structured and free-play modes. Due to the unique nature of the interface, validating the level’s effectiveness in reaching a training goal can be difficult by traditional observation methods. Level design and the challenges of learning the interface have pressed for new methods to validate the training prior to deployment. This research seeks to determine if machine learning agents can be used to validate design decisions in a training game for teaching children to utilize electromyographic prostheses, to ensure the game is a more accurate measure of participant abilities and to avoid negative training. Methods This research explores integration of a customized AI training program to aid in improvement of game design and efficacy. The Program for Autonomous Unity Learning (PAUL) is a machine learning agent that uses reinforcement learning to optimize its path through a prescribed obstacle course by determining the best electromyographic biosensing input level and timing for each obstacle in the character’s path. Results Training reward value begins approaching the end of its improvement at around 1 million steps. The average reward was approximately 140 out of a possible maximum score of 150 or approximately 96.67% of optimal play. Conclusion PAUL can determine whether obstacles within the game are technically possible for completion by players. Through this method, the game becomes more accurate as a measure of participant evaluation. Future uses of PAUL include validation of other training games developed to ensure games are playable.

Keywords: EMG; machine learning agent; training program; serious games (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:simgam:v:56:y:2025:i:3:p:326-343

DOI: 10.1177/10468781251325114

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