Adaptive myoelectric control applied to video game. (April 2015) - Oskoei, Mohammadreza Asghari; Hu, Huosheng
Highlights: We proposed adaptive schemes for myoelectric control applied to a video game. Both unsupervised and supervised adaptive schemes improve the performance. Both cope with time-related steady state deviations in myoelectric signal patterns. They keep the performance stable in long-term applications. Adaptive schemes improve game scores by 10% and reduce the high-risk errors by 40%. Abstract: This paper proposes adaptive schemes to cope with time-related changes in muscle activities during playing video game. A myoelectric control with the core of support vector machine is applied to manipulate a car in a computer-based video game. The proposed adaptive schemes model fatigue-based changes in myoelectric signals and modify the classification criteria to keep stable performance in long-term operations. Both unsupervised and supervised methods were applied to detect time-related steady state deviations in myoelectric signal patterns. Both methods improve the performance of myoelectric control and keep it stable in long-term applications.