Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7410 |
| _version_ | 1797433009112088576 |
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| author | Yin-Lin Chen Wen-Jyi Hwang Tsung-Ming Tai Po-Sheng Cheng |
| author_facet | Yin-Lin Chen Wen-Jyi Hwang Tsung-Ming Tai Po-Sheng Cheng |
| author_sort | Yin-Lin Chen |
| collection | DOAJ |
| description | This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired. |
| first_indexed | 2024-03-09T10:11:00Z |
| format | Article |
| id | doaj.art-eb8db765b4844b2a9f7675457ae50e6c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:11:00Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb8db765b4844b2a9f7675457ae50e6c2023-12-01T22:49:02ZengMDPI AGApplied Sciences2076-34172022-07-011215741010.3390/app12157410Sensor-Based Hand Gesture Detection and Recognition by Key IntervalsYin-Lin Chen0Wen-Jyi Hwang1Tsung-Ming Tai2Po-Sheng Cheng3Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 116, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 116, TaiwanNVIDIA AI Technology Center, Taipei 114, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 116, TaiwanThis study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired.https://www.mdpi.com/2076-3417/12/15/7410hand gesture detectionhand gesture recognitionneural networkshuman–machine interface |
| spellingShingle | Yin-Lin Chen Wen-Jyi Hwang Tsung-Ming Tai Po-Sheng Cheng Sensor-Based Hand Gesture Detection and Recognition by Key Intervals Applied Sciences hand gesture detection hand gesture recognition neural networks human–machine interface |
| title | Sensor-Based Hand Gesture Detection and Recognition by Key Intervals |
| title_full | Sensor-Based Hand Gesture Detection and Recognition by Key Intervals |
| title_fullStr | Sensor-Based Hand Gesture Detection and Recognition by Key Intervals |
| title_full_unstemmed | Sensor-Based Hand Gesture Detection and Recognition by Key Intervals |
| title_short | Sensor-Based Hand Gesture Detection and Recognition by Key Intervals |
| title_sort | sensor based hand gesture detection and recognition by key intervals |
| topic | hand gesture detection hand gesture recognition neural networks human–machine interface |
| url | https://www.mdpi.com/2076-3417/12/15/7410 |
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