RPCRS: Human Activity Recognition Using Millimeter Wave Radar

Millimeter wave radar-based human activity recognition (HAR) technology has received much attention as a research hot-spot in recent years. Previous researches have demonstrated the feasibility of using millimeter wave radar for HAR. While existing work has achieved excellent performance in ideal environments, its application in life is still limited due to the intensive data collection required, the additional training needed to adapt to new domains (i.e., environments, people, and locations), and the high computational complexity associated with voxelization. To solve the above problems, we propose the radar point cloud recognition system RPCRS, which is capable of accurately recognizing human activities from noisy environments, has promising recognition performance for new users, environments and locations, and significantly reduces the computational overhead during system training. Firstly, RPCRS use the velocity information of the clustered point cloud data to extract the human activity subjects from the noisy background. Then, the size of the extracted non-uniform point cloud data is unified by removing or adding the number of point clouds. Secondly, in order to enhance the robustness of the system and reduce the data collection effort, we designed a data enhancement framework based on correlation between point cloud data and human activity changes. Finally, a lightweight neural network based on a multilayer perceptron (MLP) is used to classify the raw point cloud data of human activities, which reduces the computational complexity and memory requirements associated with voxelization. We evaluate our system with 5 different activities, which attains average accuracy of 95.40%. In addition, we evaluate the performance of the system in a new environment and with new users, which obtains an average accuracy of 94.53% and 95.08%, respectively.