## 1 Introduction  
With the fast development of communication networks, such as 5G and WiFi networks, millimeter waves have emerged as a key technology to achieve high data rate transmission. With the increase in frequency, the perception ability of wireless signal improves. In the future, wireless signals will not only have communication ability but also have sensing ability, such as millimeter waves in 5G and Terahertz in 6G. Compared with the traditional communication technologies, millimeter waves have the capability of sensing. Hence, wireless sensing based on millimeter waves has attracted more research attention. Human activity recognition is widely investigated in the field of ubiquitous sensing based on inertial sensors, computer vision, or LiDAR. Compared with those traditional approaches, human activity recognition based on millimeter-wave radar has many unique advantages. Firstly, compared with LiDAR, millimeter-wave radar has a better penetration ability of floating particles such as smoke and dust. This advantage means that the process of activity recognition of millimeter-wave radar has strong environmental adaptability. Secondly, because the millimeter-wave radar uses radio frequency signals, it will not return the privacy screen of the target person, which means that the radar monitoring will not reveal personal identity information. This edge enables indoor activity recognition based on the millimeter-wave radar more widely applicable, such as nursing homes, kindergartens, wards, etc. In previous studies, researchers generally used collected data such as point cloud, range-Doppler, and echo intensity from millimeter-wave radar echo signals for activity recognition. For instance, RadHAR [1] shows how to voxelize sparse and non-uniform point clouds and feed them into a classifier. Additionally, in [2], since the single-sensor system can only observe the radial component of the micro-Doppler signal, researchers propose a multi-static sensor consisting of two bistatic micro-Doppler sensors to enhance the classification of micro-Doppler signatures to improve activity recognition accuracy. In [3], also using Doppler information, the researchers propose an environmental impact mitigation method by analyzing millimeter-wave signals in a Doppler-range domain. In terms of extracting activity features, deep learning is favored by researchers [4], among which convolutional neural networks (CNN) and long short-term memory networks (LSTM) are widely used. Because most human actions have time-series characteristics, these two network models are often combined. For example, some researchers [5] adopt LSTM + CNN network to classify activities. However, most of these studies only focus on a single type of data that cannot comprehensively depict the activity feature. The approaches based on point clouds merely leverage the distributions of point clouds in 3D space but do not consider the moving speed. In contrast, the methods using range-Doppler does not take the distributions of point clouds into consideration. Our goal is to design a multi-model network by fusing multiple types of input data to increase the recognition accuracy based on mmWave. We are going to extract the features from the two kinds of data, fuse them, and then classify the activities. Therefore, in this work, we propose a new method for activity recognition that uses both 3D point cloud data and range-Doppler data as input. We summarize the main contributions in this work as follows. * First, we propose a multi-model deep learning model to fuse the point cloud and range-Doppler to achieve activity recognition based on millimeter-wave radar. We voxelize and merge the 3D point cloud data in multiple frames into a 4D array. We further merge and convert the range-Doppler data in the same number of frames into a 3D array. Afterward, we use two sub-networks to train these two kinds of data separately. The 3D CNN + LSTM network is applied for the point cloud data, while the 3D CNN network is used for the range-Doppler data.