Machine learning-enhanced gesture recognition through impedance signal analysis Cover

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Machine learning-enhanced gesture recognition through impedance signal analysis

Journal of Electrical Bioimpedance

Volume 15 (2024): Issue 1 (January 2024)

By: Hoang Nhut Huynh, Quoc Tuan Nguyen Diep, Minh Quan Cao Dinh, Anh Tu Tran, Nguyen Chau Dang, Thien Luan Phan, Trung Nghia Tran and Congo Tak Shing Ching

Open Access

|Jun 2024

Figures & Tables

An overview of the gesture recognition system.

Evaluation of five hand gestures designed for holding, grasping, and relaxing activities.

The precision impedance analyzer WK6632.

The electrode for gesture recognition data acquisition.

Electrode configuration for impedance data collection: using four electrodes (E1-E2, E1-E3, E1-E4, E2-E3, E2-E4, and E3-E4).

Comparative SNR performance graph for five hand gestures (A, B, C, D, and E), with each gesture measured for 208 values (04 electrodes) and conducted with 200 repetitions.

The measurements obtained from electrode pairs across various gestures for three volunteers with 5 gestures A, B, C, D, and E: (a) Person 1, (b) Person 2, (c) Person 3.

Investigating PCA for gesture recognition with 2 dimensions.

Investigating PCA for gesture recognition with three dimensions.

Standard deviation values for sample data from 3 volunteers with labeled gestures A, B, C, D, and E for Person 1, Person 2, and Person 3.

Confusion matrices demonstrating the classification accuracy of machine learning models for hand gesture recognition.

Performance evaluation of machine learning models_

Model	Fold	Accuracy	Precision	Recall	F1-Score
KNN	Fold-1	0.70	0.72	0.70	0.72
	Fold-2	0.93	0.96	0.93	0.92
	Fold-3	0.91	0.92	0.91	0.90
	Fold-4	0.94	0.96	0.94	0.94
	Fold-5	0.81	0.87	0.81	0.79
	Mean	0.86	0.89	0.86	0.85
GBM	Fold-1	0.94	0.96	0.94	0.94
	Fold-2	0.93	0.94	0.93	0.92
	Fold-3	0.76	0.63	0.76	0.68
	Fold-4	0.95	0.95	0.95	0.95
	Fold-5	0.70	0.79	0.70	0.69
	Mean	0.86	0.86	0.86	0.84
Naive Bayes	Fold-1	0.72	0.73	0.72	0.71
	Fold-2	0.93	0.96	0.93	0.92
	Fold-3	0.96	0.97	0.96	0.96
	Fold-4	0.91	0.95	0.91	0.89
	Fold-5	0.70	0.74	0.70	0.67
	Mean	0.84	0.87	0.84	0.83
LR	Fold-1	0.80	0.79	0.80	0.78
	Fold-2	0.94	0.96	0.94	0.94
	Fold-3	0.93	0.94	0.93	0.92
	Fold-4	0.93	0.95	0.93	0.92
	Fold-5	0.85	0.89	0.85	0.83
	Mean	0.89	0.90	0.89	0.88
Random Forest	Fold-1	0.74	0.74	0.74	0.73
	Fold-2	0.93	0.96	0.93	0.92
	Fold-3	0.96	0.97	0.96	0.96
	Fold-4	0.93	0.96	0.93	0.92
	Fold-5	0.81	0.89	0.81	0.79
	Mean	0.87	0.90	0.87	0.86
SVM	Fold-1	0.72	0.73	0.72	0.71
	Fold-2	0.94	0.96	0.94	0.94
	Fold-3	0.94	0.95	0.94	0.94
	Fold-4	0.94	0.96	0.94	0.94
	Fold-5	0.80	0.86	0.80	0.78
	Mean	0.87	0.89	0.87	0.86

DOI: https://doi.org/10.2478/joeb-2024-0007 | Journal eISSN: 1891-5469

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Language: English

Page range: 63 - 74

Submitted on: Mar 31, 2024

Published on: Jun 11, 2024

Published by: University of Oslo

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Gesture Recognition,

Impedance Signal Spectrum Analysis (ISSA),

Machine Learning

Related subjects:

Bioengineering and biomedical engineering,

Biomedical electronics,

Biomedical engineering,

Spectroscopy and metrology

© 2024 Hoang Nhut Huynh, Quoc Tuan Nguyen Diep, Minh Quan Cao Dinh, Anh Tu Tran, Nguyen Chau Dang, Thien Luan Phan, Trung Nghia Tran, Congo Tak Shing Ching, published by University of Oslo
This work is licensed under the Creative Commons Attribution 4.0 License.

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