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Evaluation of surface EMG-based recognition algorithms for decoding hand movements
RISE - Research Institutes of Sweden (2017-2019), ICT, Acreo. Mälardalen University, Sweden.
Mälardalen University, Sweden.
Auckland University of Technology, New Zealand.
Chalmers University of Technology, Sweden.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 58, nr 1, s. 83-100Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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. [Figure not available: see fulltext.].

sted, utgiver, år, opplag, sider
Springer , 2019. Vol. 58, nr 1, s. 83-100
Emneord [en]
Classification, Dimensionality reduction, Electromyography, Feature extraction, Myoelectric pattern recognition, Classification (of information), Decoding, Discriminant analysis, Maximum likelihood estimation, Myoelectrically controlled prosthetics, Nearest neighbor search, Pattern recognition, Support vector machines, Correlation coefficient, Hjorth parameters, K-nearest neighbors, Linear discriminant analysis, Motion recognition, Recognition algorithm, Root Mean Square, Motion estimation, article, controlled study, hand movement, k nearest neighbor, maximum likelihood method, motion, reaction time, support vector machine, waveform
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Identifikatorer
URN: urn:nbn:se:ri:diva-41911DOI: 10.1007/s11517-019-02073-zScopus ID: 2-s2.0-85075364573OAI: oai:DiVA.org:ri-41911DiVA, id: diva2:1378384
Tilgjengelig fra: 2019-12-13 Laget: 2019-12-13 Sist oppdatert: 2020-06-04bibliografisk kontrollert

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