Novel Approach for Gesture Recognition Using mmWave FMCW RADAR

Hand gesture recognition driven by RADAR technology has attracted significant attention in recent years. Among various RADAR types, frequency-modulated continuous-wave (FMCW) RADAR is used in this work due to its very high range and velocity resolution. However, data collected by RADAR are disturbed by static background and static clutter. Therefore, a novel data preprocessing approach is proposed to remove the static background and clutter in the acquired data. A convolutional neural network is used to extract the features of the acquired data set. To the best of our knowledge, this is the first time that range, velocity and angle features are combined in one map, forming the input signal of a convolutional neural network. Classifiers are applied to recognize gestures. Experimental results show that the proposed method using the XGBoost classifier can achieve a high recognition accuracy of 98.93% on the test set. In contrast, the proposed method with the random forest classifier can achieve a recognition rate of 100% on the same test set with six dynamic hand gestures. This approach could be useful in aspects such as in-car entertainment systems and smart homes.