<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.</p>
</abstract>

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.

Machine learning-enhanced gesture recognition through impedance signal analysis

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Machine learning-enhanced gesture recognition through impedance signal analysis"}

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative...

Machine learning-enhanced gesture recognition through impedance signal analysis

Published Online: Jun 11, 2024

Page range: 63 - 74

Received: Mar 31, 2024

DOI: https://doi.org/10.2478/joeb-2024-0007

Keywords
Bio-impedance, Gesture Recognition, Impedance Signal Spectrum Analysis (ISSA), Machine Learning

© 2024 Hoang Nhut Huynh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Machine learning-enhanced gesture recognition through impedance signal analysis

Published Online: Jun 11, 2024

Page range: 63 - 74

Received: Mar 31, 2024

DOI: https://doi.org/10.2478/joeb-2024-0007

KeywordsBio-impedance, Gesture Recognition, Impedance Signal Spectrum Analysis (ISSA), Machine Learning

© 2024 Hoang Nhut Huynh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Bio-impedance, Gesture Recognition, Impedance Signal Spectrum Analysis (ISSA), Machine Learning