This work has been conducted in the context of pattern-recognition-based control for electromyographic prostheses. It presents a k-nearest neighbour (kNN) classification technique for gesture recognition, extended by a proportionality scheme. The methods proposed are practically implemented and validated. Datasets are captured by means of a state-of-the-art 8-channel electromyography (EMG) armband positioned on the forearm. Based on this data, the influence of kNN’s parameters is analyzed in pilot experiments. Moreover, the effect of proportionality scaling and rest thresholding schemes is investigated. A randomized, double-blind user study is conducted to compare the implemented method with the state-of-research algorithm Ridge Regression with Random Fourier Features (RR-RFF) for different levels of gesture exertion. The results from these experiments show a statistically significant improvement in favour of the kNN-based algorithm.

KNN Learning Techniques for Proportional Myocontrol in Prosthetics / Sziburis, Tim; Nowak, Markus; Brunelli, Davide. - STAMPA. - 28:(2022), pp. 679-683. [10.1007/978-3-030-70316-5_109]

KNN Learning Techniques for Proportional Myocontrol in Prosthetics

Brunelli, Davide
2022-01-01

Abstract

This work has been conducted in the context of pattern-recognition-based control for electromyographic prostheses. It presents a k-nearest neighbour (kNN) classification technique for gesture recognition, extended by a proportionality scheme. The methods proposed are practically implemented and validated. Datasets are captured by means of a state-of-the-art 8-channel electromyography (EMG) armband positioned on the forearm. Based on this data, the influence of kNN’s parameters is analyzed in pilot experiments. Moreover, the effect of proportionality scaling and rest thresholding schemes is investigated. A randomized, double-blind user study is conducted to compare the implemented method with the state-of-research algorithm Ridge Regression with Random Fourier Features (RR-RFF) for different levels of gesture exertion. The results from these experiments show a statistically significant improvement in favour of the kNN-based algorithm.
2022
Converging Clinical and Engineering Research on Neurorehabilitation 4th: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020
Switzerland
Springer
978-3-030-70315-8
978-3-030-70316-5
Sziburis, Tim; Nowak, Markus; Brunelli, Davide
KNN Learning Techniques for Proportional Myocontrol in Prosthetics / Sziburis, Tim; Nowak, Markus; Brunelli, Davide. - STAMPA. - 28:(2022), pp. 679-683. [10.1007/978-3-030-70316-5_109]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/320682
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