Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network

David Leserri, Nils Grimmelsmann, Malte Mechtenberg, Hanno Gerd Meyer and Axel Schneider
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David Leserri: Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Interaktion 1, D-33619 Bielefeld, Germany
Nils Grimmelsmann: Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Interaktion 1, D-33619 Bielefeld, Germany
Malte Mechtenberg: Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Interaktion 1, D-33619 Bielefeld, Germany
Hanno Gerd Meyer: Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Interaktion 1, D-33619 Bielefeld, Germany
Axel Schneider: Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Interaktion 1, D-33619 Bielefeld, Germany

Mathematics, 2022, vol. 10, issue 6, 1-21

Abstract: Limb movement prediction based on surface electromyography (sEMG) for the control of wearable robots, such as active orthoses and exoskeletons, is a promising approach since it provides an intuitive control interface for the user. Further, sEMG signals contain early information about the onset and course of limb movements for feedback control. Recent studies have proposed machine learning-based modeling approaches for limb movement prediction using sEMG signals, which do not necessarily require domain knowledge of the underlying physiological system and its parameters. However, there is limited information on which features of the measured sEMG signals provide the best prediction accuracy of machine learning models trained with these data. In this work, the accuracy of elbow joint movement prediction based on sEMG data using a simple feedforward neural network after training with different single- and multi-feature sets and data segmentation parameters was compared. It was shown that certain combinations of time-domain and frequency-domain features, as well as segmentation parameters of sEMG data, improve the prediction accuracy of the neural network as compared to the use of a standard feature set from the literature.

Keywords: limb movement prediction; surface electromyography; EMG; wearable robotics; feature engineering; neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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