Attribute-driven granular model for EMG-based pinch and fingertip force grand recognition

Fine multi-functional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This study proposed an attribute-driven granular model (AGrM) under a machine learning scheme to solve this problem. The model utilises the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, sixteen channels of surface electromyographic signals (i.e. main-attribute) and continuous fingertip force (i.e. sub-attribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type, and received more than 90% force grand prediction accuracy at any granular level greater than six. Further sensitivity analysis verified its robustness with respect to different channel combination and interferences. In the comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.