# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 1 Introduction  
The demand for human action recognition is increasing with the continuous development of Internet of Things technology. Human action recognition has a broad application prospect in human-computer interactions, smart homes, medical and health care, etc. More accurate human activity recognition will also give people a more convenient, comfortable lifestyle and intelligent experience. Traditional human activity recognition is mainly realized using camera devices [1], wearable devices [2, 3], infrared sensors [4, 5], and other devices. Using camera devices can achieve a high accuracy rate, but is prone to user privacy leakage. Camera devices are also susceptible to weather and environmental influences, such as dark environments, and weather conditions, such as rain, snow, and haze. Infrared devices can be used in dark environments, but are less sensitive to other heat and light sources. Wearable devices require users to wear them for long periods, making providing a comfortable and convenient experience difficult. Wearable devices are usually one-person-one-device, so they are costly and cannot be used for multi-person identification, as shown in Table 1.  
In contrast, researchers are giving more and more attention to wireless sensing devices due to the advantages of their low cost, non-invasiveness, and privacy protection. In recent years, due to the ubiquitous nature of WIFI signals [6], many studies have used WIFI signals for human action recognition [7]. [PERSON], [PERSON] et al. showed how to use a WIFI router to detect if a human falls [8]. [PERSON], [PERSON] et al. used channel state information differences for human action recognition [9]. However, the bandwidth of WIFI signals is narrow, and more than its resolution is needed to accomplish high-precision and high-robustness classification. Moreover, most WIFI-based human action recognition uses channel state information (CSI), which cannot perform multi-target human action recognition. Millimeter-wave radar can provide a higher spatial resolution due to its ultra-high bandwidth (30-300 GHz). Compared to infrared and laser equipment, millimeter-wave radar can penetrate fog, smoke, and dust and is characterized by its all-weather, round-the-clock capability [10]. In addition, millimeter-wave radar also has some imaging capabilities. [PERSON] et al. collected data using millimeter-wave radar and extracted micro-Doppler features for human action recognition [11]. [PERSON] [12] generated human body point clouds using millimeter-wave radar, and then input deep convolutional neural networks for recognition after voxelizing the point clouds. In addition, several studies have used graph neural networks (GNNs) to classify point clouds, such as Point-GNN [13]. [PERSON] et al. designed a time-distributed MMPint-GNN + bidirectional LSTM activity recognition framework. The methods for extracting micro-Doppler features in the previous studies have not fully utilized the spatial characteristics of human actions and are yet to be able to classify multiple people's actions. The methods that voxelize the point clouds that use higher resolutions result in most voxels being empty. Moreover, the computational overhead of 3D convolutional networks is very high [14]. Designing a millimeter-wave radar system that can recognize multiple people's actions simultaneously with a low recognition cost has become a vital issue. In this paper, we propose an overall framework for multi-person action recognition using millimeter-wave radar, including the following four parts: data preprocessing, target segmentation, data optimization for dimensionality reduction, and a convolutional neural network + Long Short-Term Memory (CNN + LSTM) deep learning model. First, the point cloud generated by millimeter-wave radar is sparse [15] and cannot generate a dense and uniform point cloud like LIDAR. Moreover, millimeter-wave radar is susceptible to the environment's multipath effect in data acquisition [16], so the generated point cloud data will contain much noise.