Abstract

Large variations in Surface Electromyogram (SEMG) signal across different subjects make the process of automated signal classification as a generalized tool, challenging. In this paper, we propose a domain adaptation methodology that addresses this challenge. In particular we propose a hierarchical sample selection methodology, that selects samples from multiple training subjects, based on their similarity with the target subject at different levels of granularity. We have validated our framework on SEMG data collected from 8 people during a fatiguing exercise. Comprehensive experiments conducted in the paper demonstrate that the proposed method improves the subject independent classification accuracy by 21% to 23% over the cases without domain adaptation methods and by 14% to 20% over the existing state-of-the-art domain adaptation methods.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages7853-7856
Number of pages4
DOIs
StatePublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Chattopadhyay, R., Krishnan, N. C., & Panchanathan, S. (2011). Hierarchical domain adaptation for SEMG signal classification across multiple subjects. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 7853-7856). [6091935] https://doi.org/10.1109/IEMBS.2011.6091935