Authors:
Tim Sziburis
1
;
Markus Nowak
2
and
Davide Brunelli
3
Affiliations:
1
Department of Computer Science, Ruhr West University of Applied Sciences, 45407 Mülheim an der Ruhr, Germany
;
2
Institute of Robotics and Mechatronics, German Aerospace Center (DLR), 82234 Weßling, Germany
;
3
Department of Industrial Engineering, DII, University of Trento, 38123 Trento, Italy
Keyword(s):
Surface Electromyography, Embedded Systems, Wearable Systems, Prototype Reduction, Dataset Reduction, Instance-based Learning, Gesture Recognition, Machine Learning, Prosthetics.
Abstract:
Current systems of electromyographic prostheses are controlled by machine learning techniques for gesture detection. Instance-based learning showed promising results concerning classification accuracy and robustness without explicit model training. However, it suffers from high computational demands in the prediction phase, which can be problematic in real-time scenarios. This paper aims at combining such learning schemes with the concept of prototype reduction to decrease the amount of data processed in each prediction step. First, a suitability assessment of state-of-research reduction algorithms is conducted. This is followed by a practical feasibility analysis of the approach. For this purpose, several datasets of signal classes from exerting specific gestures are captured with an eight-channel EMG armband. Based on the recorded data, prototype reduction algorithms are comparatively applied. The dataset reduction is characterized by the time needed for reduction as well as the po
ssible data reduction rate. The classification accuracy when using the reduced set in cross-validation is analyzed with an exemplary kNN classifier. While showing promising values in reduction time as well as excellent classification accuracy, a reduction rate of over 99% can be achieved in all tested gesture configurations. The reduction algorithms LVQ3 and DSM turn out to be particularly convenient.
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