# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
## 3 Materials and Methods
### Experimental Scheme  
A human body activity model is built first. When the sensor is placed vertically, the three-dimensional coordinates of the human body are shown in Figure 1, wherein the axis Y is perpendicular to the ground, representing the up-down direction of the human body; the axis X represents the front-back direction of the human body and the axis Z represents the left-right direction of the human body.  
At present, most of the fall detection research uses the data obtained from young people imitating the movements of the elderly in a laboratory environment. However, the research shows that the acceleration amplitude of the elderly is much smaller than that of the young people during walking, mainly because the physical function of the elderly declines, and their movements are sluggish. Therefore, in the absence of real fall data of the elderly, subjects wear an elderly life simulation experience suit, equipped with wrist guards at the knees, elbows and wrists to restrict joint activities and sandbags tied at the feet and wrists to simulate the state of the elderly, such as clumsy body, stiff limbs and sluggish movements, to make the young people imitate the movements of the elderly more realistically in the laboratory environment and to avoid unnecessary injuries. The LPMS-B2 posture sensor is used in the experiment, and the equipment parameters are shown in Table 1.  
Figure 1: Human activity model.  
The sensor communicates with the computer through Bluetooth and collects data at a frequency of 200 Hz, including acceleration data acquired in a range of \(\pm\)16 g and angular velocity data acquired in a range of \(\pm\)2000 dps. Based on the previous experimental analysis of different wearing positions of the sensor, it is found that the accuracy rate is the highest when the device is worn at the waist, so the sensor is fixed to the middle of the waist of the subject. The experimental environment is shown in Figure 2.  
This experiment collects data on four types of falling movements, including slipping when walking, falling in a faint, falling when sitting down and falling when getting up, and six types of daily movements, including walking, jogging, jumping, going up stairs, going down stairs and sitting down. A total of 12 subjects are included in the research, 10 males and 2 females aged between 20 and 25. The sizes of the samples are shown in Table 2. Among them, there are 560 samples of falls and 500 samples of each other daily activities, with a total of 3560 samples. The acceleration and angular velocity data of various falls and daily activities are shown graphically in Figures 3 and 4, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **LPLS-B2** \\ \hline Bluetooth & Bluetooth 2.0 and BLE (Low Power/Bluetooth 4.1) \\ Communication range & \textless{}20 m \\ Output range of Euler angle & Roll: \(\pm 180^{\circ}\); Pitch: \(\pm 90^{\circ}\); Yaw: \(\pm 180^{\circ}\) \\ Resolution & \textless{}0.01\({}^{\circ}\) \\ Accuracy & \textless{}0.5\({}^{\circ}\) (Static), \(\textless{}2^{\circ}\) RMS (Dynamic) \\ Accelerometer & 3-axis, \(\pm 2/\pm 4/\pm 4/\pm 8/\pm 16\) g, 16 bits \\ Gyroscope & 3-axis, \(\pm 125/\pm 245/\pm 500/\pm 1000/\pm 2000\) dps, 16 bits \\ Rate noise density & 0.007 dps/\(\sqrt{\text{Hz}}\) \\ Maximum sampling rate & 400 Hz \\ \hline \end{tabular}
\end{table}
Table 1: LPMS-B2 main performance parameters.  
Figure 2: Subjects wore devices for simulated experiments.