ABSTRACT 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. INDEX TERMS Ultrasonic active sensing, range-Doppler, micro hand gesture recognition, hidden Markov model, neural network.
Recently, Sang et al. REF used micro dynamic hand gestures for recognition to realize human-computer interaction.
Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing
IEEE.Access. 6(2018)49339-49347
Computer Science Engineering