# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 2 System Framework Design
### Data Preprocessing  
Because millimeter-wave radar is sensitive to small vibrations and susceptible to environmental influences, it is easy to generate multipath effects in indoor environments. Therefore, the generated point cloud data contain much noise, significantly affecting the system's stability and accuracy. In this paper, we initially used pass-through filters and statistical filters to filter the point cloud data. Pass-through filters and statistical filters have been widely used in point cloud filtering. A pass-through filter sets a range on the attributes of the point cloud (x, y, z, v, and the signal-to-noise ratio) based on the attributes of the point cloud, removes the outliers, and retains the points within the range. The whole process of a human action usually occurs in a specific spatial range, but not all parts of the human body are in motion during the whole process. Thus, in this paper, the four attributes of the point cloud's x, y, z, and signal-to-noise ratio are selected as the setup conditions of straight pass filtering. The statistical filter principle is based on the concept of statistics to identify and filter out the outliers or noise by calculating the statistical properties of the data set. The statistical filter principle used in this paper is as follows:  
1. Calculate the statistical properties of the data set, including the mean and standard deviation. The mean is the average value of the data set, and the standard deviation is the dispersion of the data set values. 2. Based on the mean and standard deviation, a threshold range is defined within which the data points are considered normal, while the data points outside this range are considered outliers. 3. By comparing each data point's mean and standard deviation with the corresponding variable, the data points that fall within the threshold range are retained, while those that fall outside the range are filtered out. The above steps describe the initial filtering of the point cloud data. Human body action is a continuous and smooth process, and the millimeter-wave radar will collect a certain number of frames in a specified time during the data acquisition process. Although there are differences in the spatial distribution of the point cloud between neighboring frames due to variations in human actions, the differences between them are limited and follow certain statistical principles. Based on this idea, we propose a filtering method based on the difference between the frames. The detailed steps are as follows:  
1. Calculate the range difference in the spatial coordinates of the point cloud between the neighboring frames: For each point's spatial coordinates (x, y, z), calculate the difference between the neighboring frames. This results in a vector representing the variation between the frames. Figure 1: System framework diagram. 2. Select the median as a benchmark: Since the human body is usually at rest in the beginning and end frames, the effect of these frames on overall variation may be significant. The median of the range differences of all the neighboring frames is chosen as the benchmark to eliminate this effect. The median can reduce the effect of outliers to a certain extent and, more accurately, reflect the general trend of change between the frames. 3. Flag and delete the point clouds whose range of variation exceeds the benchmark: Iterate through all the neighboring frames, flag them as noisy, and delete the points whose range of point cloud spatial coordinates exceeds the benchmark. Doing so eliminates the points whose range of variation between frames is too extensive, as they may be caused by noise or other disturbances.