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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Evaluation of Surface EMG-based Recognition Algorithms for Decoding Hand Movements

Abbaspour, S; Lindén, M; Gholamhosseini, H; Naber, A; Ortiz-Catalan, M
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http://hdl.handle.net/10292/14035
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Abstract
Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.
Keywords
Classification; Dimensionality reduction; Electromyography; Feature extraction; Myoelectric pattern recognition
Date
2020
Source
Medical and Biological Engineering and Computing, 58, 83–100 (2020). https://doi.org/10.1007/s11517-019-02073-z
Item Type
Journal Article
Publisher
Springer Verlag
DOI
10.1007/s11517-019-02073-z
Publisher's Version
https://link.springer.com/article/10.1007%2Fs11517-019-02073-z
Rights Statement
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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