Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth.

ABSTRACT: In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (H

SUBMITTER: Olsson AE 

PROVIDER: S-EPMC6510898 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature