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

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dc.contributor.author Jain, Sarthak
dc.contributor.author Singhal, Girish
dc.contributor.author Smith, R.J.
dc.contributor.author Kaliki, R.
dc.contributor.author Thakor, Nitish V.
dc.contributor.other Proceedings of the 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2012)
dc.coverage.spatial Rome, IT
dc.date.accessioned 2014-04-23T17:30:23Z
dc.date.available 2014-04-23T17:30:23Z
dc.date.issued 2012-06-24
dc.identifier.citation Jain, Sarthak and Singhal, Girish, “Improving long term myoelectric decoding, using an adaptive classifier with label correction”, in Proceedings of the 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2012), Rome, IT, June 24-27, 2012. en_US
dc.identifier.uri http://dx.doi.org/10.1109/BioRob.2012.6290901
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/1100
dc.description.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. en_US
dc.description.statementofresponsibility by Sarthak Jain and Girish Singhal
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en_US
dc.subject Accuracy en_US
dc.subject Adaptive systems en_US
dc.subject Decoding en_US
dc.subject Electrodes en_US
dc.subject Electromyography en_US
dc.subject Entropy en_US
dc.subject Training en_US
dc.title Improving long term myoelectric decoding, using an adaptive classifier with label correction en_US
dc.type Article en_US


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