Abstract Decoding using Bayesian Muscle Activation Estimators

Matthew Dyson, Kianoush Nazarpour

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

Abstract

Two recursive Bayesian muscle activation estimators were compared against standard linear filtering during use of a myoelectric abstract decoder. The decoder was controlled by intrinsic muscles of the hand. In both experiments the linear filter outperformed the Bayesian methods in terms of general score. The Bayesian muscle decoders were faster to respond to changes in muscle activity and show promise for significantly enhancing overall decoder communication rate.
Original languageEnglish
Title of host publication2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2112-2115
Number of pages4
ISBN (Electronic)978-1-5386-3646-6
ISBN (Print)978-1-5386-3647-3
DOIs
Publication statusPublished - 29 Oct 2018
Event40th International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, United States
Duration: 17 Jul 201821 Jul 2018
https://embc.embs.org/2018/

Publication series

Name
PublisherIEEE
ISSN (Print)1557-170X
ISSN (Electronic)1558-4615

Conference

Conference40th International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2018
CountryUnited States
CityHonolulu
Period17/07/1821/07/18
Internet address

Fingerprint Dive into the research topics of 'Abstract Decoding using Bayesian Muscle Activation Estimators'. Together they form a unique fingerprint.

Cite this