Gesture recognition and segmentation based on millimeter-wave radar

Nowadays, gesture recognition with radar is attracting wide attention from researchers and practitioners. The classification of an isolated and segmented gesture has been studied thoroughly. However, the detection, classification and segmentation of a series of gestures embedded in a data stream remains intractable. To address this problem, we develop a gesture recognition system based on millimetre-wave radar and deep learning. The radar measures range and Doppler features of gestures with high resolution. The data stream collected by radar is slightly pre-treated to suppress interference and extract information. A sliding window is used to slice those streams into appropriate data units, which are then fed to convolutional neural networks to estimate the probabilities of gesture types. By utilizing the change in those probabilities with time, the joint recognition and segmentation of gestures is realized. Experiments with real data shows that the recognition accuracy of 5 gestures is up to 92.48%.