# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 2 Related Work  
Many researchers have studied HAR from different aspects, such as sensors and algorithms. HAR methods can be divided into the following three categories based on the types of sensors: wearable devices, cameras, and millimeter-wave radars. The advantages and disadvantages of different sensors are shown in Table 1. In addition to the reasons listed in the table, cost is also an important and realistic factor influencing users' choice. For example, the camera-based method is usually cheaper than the millimeter-wave radar-based method, but the millimeter-wave radar-based method can better protect user privacy. The cost of a wearable device is usually more than the cost of a single camera, but users may need multiple cameras to monitor different rooms while one wearable device can fulfill a user's needs. Therefore, in the se-lection of monitoring methods, it is often necessary to consider the actual situation and needs of users. HAR based on cameras has been popular in the past. Some researchers separated the image background from the human and then used machine learning or deep learning to extract features [5; 6]. [PERSON] et al. [7] separated the person in the picture from the background and extracted the ratio of length to width of the human body to recognize standing and falling. In addition, some researchers extracted human contour features and recognized activities through changes in contour [8; 9; 10]. [PERSON] et al. [11] used an ellipse rather than a bounding box on HAR. They suggested that the direction standard deviation and ratio standard deviation of the ellipse can better recognize the fall. Meanwhile, [PERSON] et al. [12] improved this method by extracting the picture's features and using three points to represent people instead of using the bounding box. In this way, the changed information of the upper and lower parts of the human body can be easily analyzed. With the development of computer technology and deep learning, [PERSON] et al. [13] proposed an approach that used convolutional neural networks (CNN) to recognize the activities in a video sequence. [PERSON] et al. [14] used four independent CNNs to obtain multiple types of data and then combined the data with 4D-CNN for HAR. Compared with other methods, visual methods have better recognition accuracy and robustness, but the performance of cameras will decline rapidly in the dark environment. Having the camera based in certain places, such as bedrooms and bathrooms, will significantly violate personal privacy and bring moral and legal problems [15]. As a result, the usage of traditional cameras as sensors for HAR has been abandoned in recent years. Although researchers including [PERSON] and [PERSON] [16] have promoted 3D cameras, they have a limit on the use distance and can only be used within 0.4-3 m, which is not suitable for daily use. Wearable devices are also widely used for HAR, based on the principle that acceleration changes rapidly when the human body moves. There are many methods to measure the change of acceleration, such as accelerometer [17; 18], barometer [17], gyroscope [19; 20], and other sensors. In 2009, [PERSON] et al. [21] designed a fall recognition system with wearable and acceleration sensors to meet the needs of comprehensive care for the elderly. In 2015, [PERSON] et al. [22] designed an algorithm to analyze the tri-axial accelerometer, gyroscope,