Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing

TL;DR

This paper introduces a micro hand gesture recognition system using ultrasonic sensing, achieving high accuracy and low power consumption, suitable for real-time human-computer interaction applications.

Contribution

The paper presents a novel ultrasonic gesture recognition system with a state-transition HMM and an end-to-end neural network, improving accuracy and efficiency over existing methods.

Findings

Recognition accuracy of nearly 90% with HMM

Recognition accuracy of 96.32% with neural network

Real-time prototype demonstrates practical applicability

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 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.