Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements, it can also only be recognized as a single movement. Another point that cannot be overlooked is that continuous action recognition methods are able to recognize continuously changing actions but ignore the issue of whether continuous actions are legitimate or not (continuous actions obtained by stitching together multiple current actions do not conform to real time). In this paper, we propose a continuous action recognition method based on micro-Doppler features and transformer, which translates the micro-Doppler features of continuous actions into machine translation tasks and uses the idea of natural language processing (NLP) to identify continuous action. In order to judge whether the continuous action is legal or not, we also design the action state transition diagram as a constraint condition to strictly control the forward and backward actions. The experimental results show that the method proposed in this paper achieves good recognition accuracy for the recognition of a single action and can also effectively segment and recognize continuous actions.

Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements it can also only be recognized as a single movement. Therefore, in order to solve the problem that continuous human motion cannot be recognized, we propose a continuous action recognition method based on micro-Doppler features and Transformer, which translates the micro-Doppler features of continuous actions into machine translation tasks, and uses the idea of natural language processing (NLP) to identify continuous action.