Adaptive optimization of electromyographic channels for intelligent prosthetic hands based on individual differences

Intelligent prosthetic hands typically require an increase in the number of acquisition channels to improve gesture recognition accuracy, resulting in increased device complexity and cost. However, there are individual differences in muscle strength, body mass index, and exercise habits. Electromyographic prosthetic hands currently use standardized electromyographic channel configurations, which lack adaptability to individual differences. To address these issues, this paper proposes the electrode configuration adaptive optimization algorithm, which enhances and integrates traditional genetic algorithms and simulated annealing algorithms, and implements adaptive optimization solutions for different subjects. Experimental results show that the optimization outcomes differ among different subjects. Compared to a single optimization algorithm, the proposed algorithm can adaptively optimize the electrode configuration based on individual differences while ensuring recognition effectiveness, retaining electrode channel information that significantly contributes to gesture classification recognition, and meeting the stable recognition of their motion intentions by different subjects.