Authors:
Marc Tabie
1
and
Elsa Andrea Kirchner
2
Affiliations:
1
University of Bremen, Germany
;
2
University of Bremen and Robotics Innovation Center, Germany
Keyword(s):
Online EMG Onset Detection, Movement Prediction.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Real-Time Systems
Abstract:
In this work a study with 8 male subjects was conducted to compare three preprocessing methods for online capable movement prediction based on the recorded electromyogramm (EMG) signals of the right upper limb. One of the compared methods is the widely used Teager Kaiser Energy Operator (TKEO), the other two are a recently proposed method that is based on variance calculation of the signal and the standard deviation. Scope of the work was to show that fast methods, which are required for online processing, have at least the same performance as more classical approaches with higher demands on computational resources like the TKEO. An adaptive threshold was used for onset detection after preprocessing in all compared cases. Comparisons of preprocessing methods were done with respect to the performance in movement prediction and earliness of onset detection. The influence of different movement speeds on the prediction time and the performance were investigated as well. Results presented
here show significant differences between the investigated preprocessing methods concerning the prediction time. As a further result of this study it could be shown that different movement speeds also have a significant effect on the prediction time.
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