Pose Estimation from Electromyographical Data using Convolutional Neural Networks

Robin Ayling; Colin G Johnson; Ling Li; Ramaswamy Palaniappan

doi:10.1109/EMBC44109.2020.9175659

Pose Estimation from Electromyographical Data using Convolutional Neural Networks

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul:2020:653-656. doi: 10.1109/EMBC44109.2020.9175659.

Authors

Robin Ayling, Colin G Johnson, Ling Li, Ramaswamy Palaniappan

PMID: 33018072
DOI: 10.1109/EMBC44109.2020.9175659

Abstract

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6% for the EMG model, and 3.6% when accelerometry data is included. This shows that hand pose can be effectively estimated from EMG data, which can be enhanced with accelerometry data.

MeSH terms

Accelerometry
Electromyography
Hand*
Humans
Neural Networks, Computer*