Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing
In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasoni...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8453783/ |
| _version_ | 1818641597126934528 |
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| author | Yu Sang Laixi Shi Yimin Liu |
| author_facet | Yu Sang Laixi Shi Yimin Liu |
| author_sort | Yu Sang |
| collection | DOAJ |
| description | In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture classification. This method achieves a recognition accuracy of nearly 90% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32%. In addition to offline analysis, a real-time prototype is released to verify our method's potential for application in the real world. |
| first_indexed | 2024-12-16T23:29:41Z |
| format | Article |
| id | doaj.art-cdad673b7237416abd3dc3f20712f9c8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:29:41Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cdad673b7237416abd3dc3f20712f9c82022-12-21T22:11:55ZengIEEEIEEE Access2169-35362018-01-016493394934710.1109/ACCESS.2018.28682688453783Micro Hand Gesture Recognition System Using Ultrasonic Active SensingYu Sang0https://orcid.org/0000-0002-1120-0310Laixi Shi1https://orcid.org/0000-0003-4038-8620Yimin Liu2Department of Electronic Engineering, Tsinghua University, Beijing, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaIn this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture classification. This method achieves a recognition accuracy of nearly 90% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32%. In addition to offline analysis, a real-time prototype is released to verify our method's potential for application in the real world.https://ieeexplore.ieee.org/document/8453783/Ultrasonic active sensingrange-Dopplermicro hand gesture recognitionhidden Markov modelneural network |
| spellingShingle | Yu Sang Laixi Shi Yimin Liu Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing IEEE Access Ultrasonic active sensing range-Doppler micro hand gesture recognition hidden Markov model neural network |
| title | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| title_full | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| title_fullStr | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| title_full_unstemmed | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| title_short | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| title_sort | micro hand gesture recognition system using ultrasonic active sensing |
| topic | Ultrasonic active sensing range-Doppler micro hand gesture recognition hidden Markov model neural network |
| url | https://ieeexplore.ieee.org/document/8453783/ |
| work_keys_str_mv | AT yusang microhandgesturerecognitionsystemusingultrasonicactivesensing AT laixishi microhandgesturerecognitionsystemusingultrasonicactivesensing AT yiminliu microhandgesturerecognitionsystemusingultrasonicactivesensing |