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A-mode Ultrasound Driven Sensor Fusion for Hand Gesture Recognition

  • University of Portsmouth

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Traditionally, Surface electromyography (sEMG) has been the predominant method of sensing muscle activity in order to control myoelectric prosthesis. While many prosthesis control schemes used simple direct control, an ever increasing focus has moved to pattern recognition based approaches which promise a greater degree of natural control such as to further improve an amputees quality of life. Although pattern recognition based approaches have shown great promise, they have innate limitations due to changes that may occur during long term use which prevent clinical acceptance. Due to these limitations, researchers have increasingly investigated alternative modalities to provide more robust control schemes. A particular modality that has seen increasing interest is ultrasound based sensing due to its capability to better understand deep tissue activity.Within this research, A-mode ultrasound based sensing is proposed not as a replacement for sEMG based sensing but instead to augment and drive sEMG based sensing during activities that may otherwise prove challenging to traditional sEMg based control schemes.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • A-mode Ultrasound sEMG Hand Motion Recognition Rehabilitation

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