Improving long term myoelectric decoding, using an adaptive classifier with label correction

Sarthak Jain, Girish Singhal, Ryan J. Smith, Rahul Kaliki, Nitish Thakor

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

Abstract

This study presents a novel adaptive myoelectric decoding algorithm for control of upper limb prosthesis. Myoelectric decoding algorithms are inherently subject to decay in decoding accuracy over time, which is caused by the changes occurring in the muscle signals. The proposed algorithm relies on an unsupervised and on demand update of the training set, and has been designed to adapt to both the slow and fast changes that occur in myoelectric signals. An update in the training data is used to counter the slow changes, whereas an update with label correction addresses the fast changes in the signals. We collected myoelectric data from an able bodied user for over four and a half hours, while the user performed repetitions of eight wrist movements. The major benefit of the proposed algorithm is the lower rate of decay in accuracy; it has a decay rate of 0.2 per hour as opposed to 3.3 for the non adaptive classifier. The results show that, long term decoding accuracy in EMG signals can be maintained over time, improving the performance and reliability of myoelectric prosthesis.

Original languageEnglish (US)
Title of host publication2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
Pages532-537
Number of pages6
DOIs
StatePublished - Oct 18 2012
Event2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012 - Rome, Italy
Duration: Jun 24 2012Jun 27 2012

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
ISSN (Print)2155-1774

Other

Other2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
CountryItaly
CityRome
Period6/24/126/27/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Improving long term myoelectric decoding, using an adaptive classifier with label correction'. Together they form a unique fingerprint.

  • Cite this

    Jain, S., Singhal, G., Smith, R. J., Kaliki, R., & Thakor, N. (2012). Improving long term myoelectric decoding, using an adaptive classifier with label correction. In 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012 (pp. 532-537). [6290901] (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics). https://doi.org/10.1109/BioRob.2012.6290901