K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor
Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bend...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/1/210 |
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| author | Sathishkumar Subburaj Chih-Ho Yeh Brijesh Patel Tsung-Han Huang Wei-Song Hung Ching-Yuan Chang Yu-Wei Wu Po Ting Lin |
| author_facet | Sathishkumar Subburaj Chih-Ho Yeh Brijesh Patel Tsung-Han Huang Wei-Song Hung Ching-Yuan Chang Yu-Wei Wu Po Ting Lin |
| author_sort | Sathishkumar Subburaj |
| collection | DOAJ |
| description | Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively. |
| first_indexed | 2024-03-11T10:03:53Z |
| format | Article |
| id | doaj.art-612de4ee8e304f3f9967df4df9b7ad89 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T10:03:53Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-612de4ee8e304f3f9967df4df9b7ad892023-11-16T15:12:38ZengMDPI AGElectronics2079-92922023-01-0112121010.3390/electronics12010210K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric SensorSathishkumar Subburaj0Chih-Ho Yeh1Brijesh Patel2Tsung-Han Huang3Wei-Song Hung4Ching-Yuan Chang5Yu-Wei Wu6Po Ting Lin7Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Applied Science and Technology, Advanced Membrane Research Center, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Applied Science and Technology, Advanced Membrane Research Center, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanRecently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively.https://www.mdpi.com/2079-9292/12/1/210human gesture recognitionK-mermachine learning |
| spellingShingle | Sathishkumar Subburaj Chih-Ho Yeh Brijesh Patel Tsung-Han Huang Wei-Song Hung Ching-Yuan Chang Yu-Wei Wu Po Ting Lin K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor Electronics human gesture recognition K-mer machine learning |
| title | K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor |
| title_full | K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor |
| title_fullStr | K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor |
| title_full_unstemmed | K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor |
| title_short | K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor |
| title_sort | k mer based human gesture recognition khgr using curved piezoelectric sensor |
| topic | human gesture recognition K-mer machine learning |
| url | https://www.mdpi.com/2079-9292/12/1/210 |
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