Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2017
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| _version_ | 1866915037645373440 |
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| author | Sang, Yu Shi, Laixi Liu, Yimin |
| author_facet | Sang, Yu Shi, Laixi Liu, Yimin |
| contents | 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 recognition. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1712_00216 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing Sang, Yu Shi, Laixi Liu, Yimin Signal Processing Human-Computer Interaction 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 recognition. 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. |
| title | Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing |
| topic | Signal Processing Human-Computer Interaction |
| url | https://arxiv.org/abs/1712.00216 |