Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition

نویسندگان

چکیده

Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and corresponding force, it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under machine-learning scheme solve this problem. The utilizes additionally captured attribute as latent variable for supervised granulation procedure. It was fulfilled EMG-based classification fingertip force grand prediction. In experiments, 16 channels of surface electromyographic signals (i.e., main attribute) continuous subattribute) were simultaneously collected while subjects performing eight types pinches. use AGrM improved recognition accuracy around 97.2% by 1.8% when constructing granules each grasping type received more than 90% prediction at any level greater six. Further, sensitivity analysis verified its robustness with respect different channel combination interferences. comparison other clustering-based methods, achieved comparable pinch but lowest computational cost highest accuracy.

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ژورنال

عنوان ژورنال: IEEE transactions on cybernetics

سال: 2021

ISSN: ['2168-2275', '2168-2267']

DOI: https://doi.org/10.1109/tcyb.2019.2931142