## 1 Introduction  
Human activity recognition (HAR) is the process of automatic detection and classification of human motion states using sensor data, machine learning, and pattern recognition. In recent years, HAR has been applied in many different fields, such as smart homes [1], healthcare [2] (monitoring the health status of elderly and disabled people), and security [3] (biometric or face recognition). Traditional human motion recognition methods include computer vision [4], wearable sensors [5], and radar [6]. For the application of computer vision in human action recognition, [PERSON] et al. [7] proposed a deep significant data-guided framework for recognizing 12 complex daily activities. [PERSON] et al. [8] proposed a deep learning neural network technique based on deep divisible convolution (DSC) and bidirectional long- and short-term memory (DSC-blstm). [PERSON] et al. [9] implemented a virtual mouse and keyboard with gesture recognition using computer vision techniques. [PERSON] et al. [10] proposed a CNN-LSTM model for gesture recognition by using visual data processing and feature extraction. [PERSON] et al. [11] reported that incorporation of transfer learning (TL) into vision-based HAR can improve the recognition performance of existing systems. However, there are still some limitations in the practical application of vision-based human activity recognition, such as not being friendly enough to user privacy and susceptibility to camera occlusion problems. For wearable sensors for human action recognition, [PERSON] et al. [12] integrated a human activity recognition system into outdoor clothing to solve the human activity recognition problem in outdoor terrain. [PERSON] et al. [13] proposed an effective bilinear spatio-temporal attention network (Bi-STAN) that adaptively focuses on the important content and location in the original data, and the results performed well on three public datasets and on their own real datasets. [PERSON] et al. [14] proposed an attentional long- and short-term memory (LSTM)-based network for wearable human activity recognition. [PERSON] et al. [15] had volunteers wear a MIMU-based device to perform seven different daily activities. The results showed that wear-based human activity recognition is feasible. [PERSON] et al. [16] proposed a deep learning assisted reconstruction encoder (UDR-RC) to optimize the data during preprocessing of wearable sensors on nodes to minimize the computation time of 11.25 ns for the test set size. These papers pointed out that there are two main limitations of wearable sensors for human action recognition: first, the device must be carried on the body, which is a poor user experience; second, the relatively high cost of the device makes it unsuitable for generalization. Radar technology can be used for human action recognition. [PERSON] et al. [17] used high-dimensional computing (HDC) for the first time in a radar system to recognize human activities and achieved 92.93% accuracy for six human activities using HDC. [PERSON] et al. [18] constructed a source hypothesis migration learning system for building environmental adaptation mechanisms for cross-environment radar HAR. [PERSON] et al. [19] proposed a time-domain three-dimensional convolutional neural network (3 DCNN) with the addition of a temporal attention module to emphasize the sequential relationship between frames. [PERSON] et al. [20] successfully detected three types of motion changes in people using FMCW radar. [PERSON] et al. [21] combined radar and cameras to analyze time-frequency images and images of objects in order to classify objects and the activities they are performing. These papers pointed out that radar-based human activity recognition required a large amount of human and material resources for data acquisition, which was difficult.