## 3 Materials and Methods  
The comparison of the advantages and disadvantages of the previously introduced methods with those of our method are shown in Table 1. To safeguard user information and offer comprehensive security protection across all time frames and areas, establishing a stable signal to enhance recognition rates is essential. Due to the increasingly complex electromagnetic environment and signals, traditional identification methods struggle to achieve desired recognition rates. This paper proposes a deep learning-based approach that analyzes the received signals to generate point cloud diagrams, enabling classification algorithms to more effectively differentiate between data points. The architecture of the nursing-secure-care system is shown in Figure 4, which is constructed by a Millimeter sensor, gesture recognition system, and IoT communication system. In this paper, we focus on how to make the point-cloud image, how to build the recognition system with YOLO, and how to make the MQTT commands to control secure devices for the embedded systems. The confirmed posture results are sent via MQTT to the Topic within the Broker, which in this case is Pi-4. Subsequently, messages are sent separately based on the subscribed content of the subscribers, enabling the rapid transmission of the current posture to users' devices. Millimeter sensors transmit electromagnetic wave signals, which are reflected by objects, similar to radar systems. By capturing reflected signals, radar systems can determine the distance, velocity, and angle of objects for micro-Doppler effects, which can be processed to provide unique data sets such as, distance, velocity, and angle, as well as the ability to distinguish different targets, which enables the sensor to detect the characteristics of different objects within the detection range. These data, for example, allow sensors to sense the echo signals--modulation effects from tiny movements--that include the typical features of objects such as, the spinning speed of a bicycle wheel, the swaying arms of a person, or the running limbs of an animal. In this project, we will build a sensing system with an AI model that can be used in the embedded systems. To approach these goals, we will go through this project as follows. First, we will generate the pixel coordinates by calculating the echo signals of the Doppler effects in period. Second, the pixel coordinates will be used to build the pixel cloud images with layer-coloring methods. Noise can be filtered with mathematical morphologies, such as erosion and dilation methods,. Third, we classify the pixel cloud images and train them with machine learning models, such as YOLO-v4, to obtain weight tables for sensing event recognition. Fourth, we simplify the machine learning models and build up the performance-oriented programs to ensure that we can run the mmWave sensing system in the embedded systems. \begin{table}
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This paper employs the TI IWR6843 AOP single-chip mmWave sensor as shown in Figure 5. This chip operates within the frequency range of 60 GHz to 64 GHz and functions adequately in general environments ranging from \(-40\) degrees to 105 degrees Celsius. It consists of 4 receiver (RX) and 3 transmitter (TX) antenna modules. The receiver operates at a Baud rate of 115,200 while the transmitter can reach up to 921,600, facilitating high-speed and precise data transmission. It offers a transmission speed of 50 ms/frame. In this study, we utilize the UART interface to connect with a computer. The received data is analyzed by the computer and plotted on the canvas (Mat window) within the program, enabling our observation. To make it easier for users to use our entire system, we assign colors to the point cloud according to the distance. When the position is far away from what we need to identify, we will lighten the color. In this way, we will tell the user where the setting needs to be adjusted. to find the best distance for attitude recognition.