Estimation of joint kinematics and fingertip forces using motoneuron firing activities: A preliminary report

Feng Xu, Yang Zheng, Xiaogang Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

A loss of individuated finger movement affects critical aspects of daily activities. There is a need to develop neural-machine interface techniques that can continuously decode single finger movements. In this preliminary study, we evaluated a novel decoding method that used finger-specific motoneuron firing frequency to estimate joint kinematics and fingertip forces. High-density electromyogram (EMG) signals were obtained during which index or middle fingers produced either dynamic flexion movements or isometric flexion forces. A source separation method was used to extract motor unit (MU) firing activities from a single trial. A separate validation trial was used to only retain the MUs associated with a particular finger. The finger-specific MU firing activities were then used to estimate individual finger joint angles and isometric forces in a third trial using a regression method. Our results showed that the MU firing based approach led to smaller prediction errors for both joint angles and forces compared with the conventional EMG amplitude based method. The outcomes can help develop intuitive neural-machine interface techniques that allow continuous single-finger level control of robotic hands. In addition, the previously obtained MU separation information was applied directly to new data, and it is therefore possible to enable online extraction of MU firing activities for real-time neural-machine interactions.

Original languageEnglish (US)
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Pages1035-1038
Number of pages4
ISBN (Electronic)9781728143378
DOIs
StatePublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/TerritoryItaly
CityVirtual, Online
Period5/4/215/6/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

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