Evaluation of surface EMG-based recognition algorithms for decoding hand movements
Artikel i vetenskaplig tidskrift, 2019

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.

Classification

Myoelectric pattern recognition

Feature extraction

Dimensionality reduction

Electromyography

Författare

Sara Abbaspour

RISE Research Institutes of Sweden

Mälardalens högskola

Maria Linden

Mälardalens högskola

Hamid Gholamhosseini

Auckland University of Technology

Autumn Naber

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Max Ortiz-Catalan

Integrum AB

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Medical and Biological Engineering and Computing

0140-0118 (ISSN)

Vol. in press

Ämneskategorier

Reglerteknik

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.1007/s11517-019-02073-z

PubMed

31754982

Mer information

Senast uppdaterat

2019-12-10