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Open AccessArticle

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

1
Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
2
Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zürich, 8008 Zürich, Switzerland
3
Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Jan Cornelis and Aime’ Lay-Ekuakille
Sensors 2021, 21(4), 1504; https://doi.org/10.3390/s21041504
Received: 21 December 2020 / Revised: 10 February 2021 / Accepted: 15 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Surface EMG and Applications in Gesture Recognition)
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement. View Full-Text
Keywords: force myography; hand gestures recognition; machine learning; data pre-processing force myography; hand gestures recognition; machine learning; data pre-processing
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MDPI and ACS Style

Asfour, M.; Menon, C.; Jiang, X. A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance. Sensors 2021, 21, 1504. https://doi.org/10.3390/s21041504

AMA Style

Asfour M, Menon C, Jiang X. A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance. Sensors. 2021; 21(4):1504. https://doi.org/10.3390/s21041504

Chicago/Turabian Style

Asfour, Mohammed; Menon, Carlo; Jiang, Xianta. 2021. "A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance" Sensors 21, no. 4: 1504. https://doi.org/10.3390/s21041504

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