Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System

Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, Yanjuan Geng, Xiangxin Li, Sandeep Pirbhulal, Shixiong Chen, Naik Ganesh, Pang Feng, Guanglin Li

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

Abstract

Electromyogram (EMG) pattern-recognition (PR) is the most widely adopted prostheses/rehabilitation robots control method that seamlessly support multi-degrees of freedom (MDF) function in an intuitive fashion. The feature extraction framework applied in such PR-based control essentially determines the control performance of the prosthetic device. Based on the drawbacks of the commonly utilized feature extraction methods, this study proposed a spatio-temporal-based feature set (STFS) that might optimally characterize EMG signal patterns even in the presence of white Gaussian noise (WGN) to realize consistently accurate and stable decoding of multiple classes of limb-movements. For benchmark evaluation, the performance of the proposed STFS method was examined in comparison to notable existing popular methods using high density surface EMG recordings from 8 amputees, with metrics such as classification error (CE) and feature-space separability index. Compared to the existing methods, the STFS recorded substantial reduction of up 16.73% even in the presence the inevitable WGN at p<0.05. Also, with principal component analysis concept, the proposed STFS feature-space indicates obvious class separability compared to the previous methods. Therefore, the newly proposed STFS method could potentially facilitate the realization of consistently accurate and reliable PR-based control for MDF prostheses/rehabilitation robots.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2637-2640
Number of pages4
ISBN (Electronic)9781538613115
ISBN (Print)9781538613122
DOIs
Publication statusPublished - 7 Oct 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period23/07/1927/07/19

Keywords

  • Signal to Noise Ratio (SNR)
  • Prosthetics
  • Electromyography
  • Feature extraction

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  • Cite this

    Samuel, O. W., Grace Asogbon, M., Geng, Y., Li, X., Pirbhulal, S., Chen, S., Ganesh, N., Feng, P., & Li, G. (2019). Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2637-2640). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857638