Riddle: Real-Time Interacting with Hand Description via Millimeter-Wave Sensor
Zhenyuan Zhang, Zengshan Tian, Mu Zhou, Wei Nie, Ze Li · 2018
In this paper, we present a Real-time Interacting with Hand Description system via Millimeter-wave Sensor (Riddle) for human-computer interaction. Firstly, we describe a new approach to developing a radar-based system. When hand motions are captured by millimeter- wave radar sensor, the unique range information can be observed in the spectrogram. Compared to traditional hand gesture recognition systems based on optical sensors, the radar-based system avoids the influence of ambient light conditions. Secondly, we employ deep neural networks combined with connectionist temporal classification algorithm to recognize diverse hand gestures in real-time. Besides, we visualize the feature maps extracted from different layers to understand the deep neural networks. The deep neural networks are powerful to extract hand gesture features as well as class boundaries through a training process. Finally, we demonstrate that Riddle is capable of detecting six hand gestures and achieving high recognition accuracy of 96%.