## 3 Materials and Methods
Through the results, it is found that this method has the following advantages. From walking (Stand), sitting (Sit), lying down (for sleep time: Lie), falling (abnormal motion detection: Fall), switching lights (Light), and help (distress: Help) gestures, such as those shown in Figure 6, all can be completed within the complete set of safety protection behavior system in our designs. Figure 4: The architecture of the proposed system. Figure 5: mmWave IWR6843 AOP module. When the mmWave detects objects by the micro-Doppler effect, the parameters of elevation, azimuth, and Doppler velocity, will be obtained. We will utilize Equations (1)-(3) to compute coordinates, thereby converting the mmWave spherical coordinates into Cartesian coordinates, as shown in Figure 7, as follows:  
\[\text{X}=\text{R}\times\text{cos(elevation)}\times\text{sin(azimuth)} \tag{1}\]  
\[\text{Y}=\text{R}\times\text{cos(elevation)}\times\text{cos(azimuth)} \tag{2}\]  
\[\text{Z}=\text{R}\times\text{sin(elevation)} \tag{3}\]  
To increase the gesture recognition rate, we propose the layer-coloring method. These points will be made as semi-coloring pixel cloud images according to the height, distance, and direction. The coloring mapped table for height is shown in Table 2. We use pixel cloud images, as illustrated in Figure 8, to create classifying and labelling as training target objects for the machine learning model. After training is complete, we can use the weight tables to make an event recognition system. To obtain the time sequence of the scenario, we grab the pixel cloud images of each frame and overlap them by an adaptive time paragraph, such as 0.2 s. The semi-coloring pixel cloud images become the input source data of the mmWave sensing system. Figure 6: The gestures for nursing-secure-care system. Figure 7: mmWave point cloud coordinates. From Table 3, the recognition rates without semi-coloring illustrate that misjudgments are serious, such that the gesture Fall can be recognized as the gesture Lie, the gesture Light, and the gesture Fall, making it impossible to accurately identify the correct one. From Table 4, the recognition rates with semi-coloring illustrate that the error rate decreases and approaches 0%, which means that the correct posture can be accurately identified. Therefore, the semi-coloring method is an excellent approach to recognizing the gestures. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Stand** & **Sit** & **Lie** & **Help** & **Light** & **Fall** \\ \hline
**Stand** & 60\% & 30\% & 0\% & 10\% & 0\% & 0\% \\ \hline
**Sit** & 30\% & 60\% & 0\% & 8\% & 2\% & 0\% \\ \hline
**Lie** & 0\% & 0\% & 40\% & 0\% & 20\% & 40\% \\ \hline
**Help** & 10\% & 8\% & 0\% & 82\% & 0\% & 0\% \\ \hline
**Light** & 0\% & 2\% & 20\% & 0\% & 50\% & 28\% \\ \hline
**Fall** & 0\% & 0\% & 40\% & 0\% & 28\% & 32\% \\ \hline \end{tabular}
\end{table} 
Table 3: The recognition rates without semi-coloring. Figure 8: mmWave semi-coloring pixel cloud images. In deep learning, the convolutional neural network (CNN) is a type of deep neural network, which is the most common mode in current applications and is best at image processing. It is inspired by the human visual nervous system and is designed using a variant of multilayer perceptron that requires minimal preprocessing, based on their shared weight architecture and translation-invariant features. The CNN method has two major characteristics:  
1. It can effectively reduce the dimensionality of pictures with large amounts of data into small amounts of data. 2. It can effectively retain image features and conform to the principles of image processing. The first problem solved by CNN is to simplify complex problems. It reduces the dimension of a large number of parameters into a small number of parameters and then processes them. It retains the characteristics of the image in a visual-like way, and when the image is flipped, rotated, or positionally changed, it can also effectively identify similar images.