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 methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA)...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-06-01
|
| Series: | Journal of Electrical Bioimpedance |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/joeb-2024-0007 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593641980297216 |
|---|---|
| author | Huynh Hoang Nhut Diep Quoc Tuan Nguyen Dinh Minh Quan Cao Tran Anh Tu Dang Nguyen Chau Phan Thien Luan Tran Trung Nghia Ching Congo Tak Shing |
| author_facet | Huynh Hoang Nhut Diep Quoc Tuan Nguyen Dinh Minh Quan Cao Tran Anh Tu Dang Nguyen Chau Phan Thien Luan Tran Trung Nghia Ching Congo Tak Shing |
| author_sort | Huynh Hoang Nhut |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c37c761b70b74a46bf684b8ac924f8e7 |
| institution | Kabale University |
| issn | 1891-5469 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Electrical Bioimpedance |
| spelling | doaj-art-c37c761b70b74a46bf684b8ac924f8e72025-01-20T11:09:56ZengSciendoJournal of Electrical Bioimpedance1891-54692024-06-01151637410.2478/joeb-2024-0007Machine learning-enhanced gesture recognition through impedance signal analysisHuynh Hoang Nhut0Diep Quoc Tuan Nguyen1Dinh Minh Quan Cao2Tran Anh Tu3Dang Nguyen Chau4Phan Thien Luan5Tran Trung Nghia6Ching Congo Tak Shing71Laboratory of Laser Technology, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam1Laboratory of Laser Technology, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam1Laboratory of Laser Technology, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam2Laboratory of General Physics, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam3Department of Telecommunication Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam5Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung402, Taiwan1Laboratory of Laser Technology, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City72409, Vietnam5Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung402, TaiwanGesture 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.https://doi.org/10.2478/joeb-2024-0007bio-impedancegesture recognitionimpedance signal spectrum analysis (issa)machine learning |
| spellingShingle | Huynh Hoang Nhut Diep Quoc Tuan Nguyen Dinh Minh Quan Cao Tran Anh Tu Dang Nguyen Chau Phan Thien Luan Tran Trung Nghia Ching Congo Tak Shing Machine learning-enhanced gesture recognition through impedance signal analysis Journal of Electrical Bioimpedance bio-impedance gesture recognition impedance signal spectrum analysis (issa) machine learning |
| title | Machine learning-enhanced gesture recognition through impedance signal analysis |
| title_full | Machine learning-enhanced gesture recognition through impedance signal analysis |
| title_fullStr | Machine learning-enhanced gesture recognition through impedance signal analysis |
| title_full_unstemmed | Machine learning-enhanced gesture recognition through impedance signal analysis |
| title_short | Machine learning-enhanced gesture recognition through impedance signal analysis |
| title_sort | machine learning enhanced gesture recognition through impedance signal analysis |
| topic | bio-impedance gesture recognition impedance signal spectrum analysis (issa) machine learning |
| url | https://doi.org/10.2478/joeb-2024-0007 |
| work_keys_str_mv | AT huynhhoangnhut machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT diepquoctuannguyen machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT dinhminhquancao machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT trananhtu machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT dangnguyenchau machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT phanthienluan machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT trantrungnghia machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis AT chingcongotakshing machinelearningenhancedgesturerecognitionthroughimpedancesignalanalysis |