## 5 Experimental Validation
### Millimeter Wave Radar Vital Sign Processing  
The radar evaluation board utilized in this experiment was an IWR1642 Boost produced by TI. The starting frequency of the transmit signal was set to 77 GHz, the FM slope was designated as 70.006 MHz/s, the number of samples per chirp was 200, the sampling rate was 4000 ksps, the frame periodicity was set to 50 ms, and each frame contained 1024 chirp signals. The mode was one send and four received. In the experiment, IWR1642 [21] and DCA1000 [22] were used to collect vital information on the streaming data mode. (1) Determine the position of the human body. Targets 1 and 2 were detected by the binocular camera, and the distances were 1.67 and 2.74 m, respectively, as shown in Figure 16.  
Figure 16: Human recognition detection. Figure 15: Binocular camera calibration. (2) Radar signal processing. In the range-dimensional FFT of the raw data received by the radar, multiple targets were in the range-dimensional FFT, as shown in Figure 17.  
Because the distance resolution of the radar was 0.049 m, the frequency spectrum was centered on 1.67 and 2.74 m, the range was \(\pm\)0.05 m to search, the point of maximum energy was taken as the target to be measured, and finally, the distances were determined to be 1.67 and 2.70 m. The spectral peak was the target to be measured and the range gate was locked, as shown in Figure 18.  
The phase information of targets 1 and 2 was extracted, and then phase unwrapping was performed and phase differences were determined to obtain the relationship between the phase and the number of sampling points, as shown in Figure 19.  
The results of medical research regarding the breathing, heartbeat displacement, and frequency parameters of ordinary adults are shown in Table 10.  
BR (0.1-0.5 Hz) and HR (0.8-2.0 Hz) filters were established; the respiratory signal was separated first, as shown in Figure 20. And its spectrum was estimated, as shown in Figure 21.  
Figure 17: Range FFT spectrum. Figure 16:0 Search results of spectrum peaks. Figure 16:1 Phase difference. (**a**) Target 1 phase difference. (**b**) Target 2 phase difference. Figure 16:2 Respiratory time-domain waveform. (**a**) Respiratory time-domain waveform of target 1. (**b**) Respiratory time-domain waveform of target 2. Figure 16:3 Phase difference. (**a**) Target 1 phase difference. (**b**) Target 2 phase difference. The life signals of the two targets could still be separated after the position changed. The farther the distance was, the smaller the detected phase change was, and the smaller the calculated amplitude was. (3) Performance comparison between respiratory and heart rate. To verify the accuracy and effectiveness of the method, the radar sensor and the binocular camera were used to collect 10 groups of 30-s heartbeat and breathing data in a two-person, general indoor environment, and the proposed method was used to calculate the breathing and heartbeat frequency. Additionally, we compared the data with the breathing frequency collected by a breathing sensor and the heartbeat frequency collected by a smart bracelet. The results are shown in Figure 16:4  
We used the Pearson correlation coefficient (PCC) to compare the data collected with our method and contact equipment [23], whereby the PCC value ranged from 0 to 100%; 0 indicates that the two are completely uncorrelated, and 100% indicates that the two are completely correlated. The results are shown in Figure 16:5  
The vital signal data calculated by using the proposed method were very close to the data detected by the wearable sensors, and the correlation was high. The matching rate (PCC value) of the breathing rate obtained with the sensor and by using the proposed method reached 84.9%, and the matching rate (PCC value) of the heartbeat frequency