Open-Set Motion Recognition and Adaptive Structural Modification of Classifiers Based on Clustering of Unknown Motions

In the development of myoelectric prosthetic hands for reliable determination of the user's intended motion, openset recognition methods enabling consideration of unexpected inputs help to prevent malfunction. Conversely, some abnormal inputs may encompass new motions that can be used for motion classifier evolution and automatic acquisition of anomaly cluster structures. Against such a background, this paper outlines advanced open-set recognition involving clustering of unknown classes for enhanced ease of prosthetic hand interface usage. The proposed approach involves open-set recognition using the authors' probabilistic neural network, a novel cluster detection method employing a non-parametric Bayesian model, and structural modification. Results from forearm motion recognition using electromyogram signals demonstrated the effectiveness of the technique.