Adapting Random Forest Classifier Based on Single and Multiple Features for Surface Electromyography Signal Recognition

Tao Zhou, Olatunji Mumini Omisore, Wenjing Du, Lei Wang, Yuan Zhang

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

5 Citations (Scopus)

Abstract

Surface Electromyography (sEMG) signals have been recently adopted in developing control models for wearable interventional robots and related surgical systems' mechanical control problems. However, adapting sEMG signal models for intravascular robotic interventions is yet to receive any attention due to inability of obtaining continuously reliable signals from the human experts and real-time decoding and integration of the control outputs into the the robotic mechanism. Pattern recognition modeling and classification are vital steps towards developing effective sEMG-based signal models for surgeon-robot intravascular procedures, and these are focused in this paper. For this purpose, nine single features and nine groups of multiple features were investigated for hand motion recognition with the random forest classifier. The proposed random forest classifier was trained and evaluated using a dataset with recordings of twelve finger motions made by ten subjects from a publicly accessible database in the Ninapro project. An average motion classification accuracy of 84.11±3.99% and a best result of subject of 92.94% was obtained from the adapted classifier. Besides this, we also employed three performance metrics to explore more performance detail of the experimental results, and to showed the learning process of the training process.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
EditorsQingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148526
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes
Event12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China
Duration: 19 Oct 201921 Oct 2019

Publication series

NameProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

Conference

Conference12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Country/TerritoryChina
CityHuaqiao
Period19/10/1921/10/19

Keywords

  • Confusion Matrix
  • Feature Extraction
  • Random Forest
  • Robotic Catheterization
  • SEMG Pattern Recognition

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Information Systems and Management

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