# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
## 4 Experimental Setup and Result Analysis  
### The Experimental Setup  
The experimental equipment is the 77 GHz millimeter wave development board IWR1642, as shown in Figure 7. The antenna is set to two transmitters and four receivers, and the frame cycle time is set to 40 ms, that is, the time length of 100 frames for each gesture. Each frame contains 128 chirped signals, and 128 sampling points are set for each chirp to realize the discretization of the IF signal. Then, the dca1000 development board is used to transmit the original data to the PC. The upper computer program reads the signal's amplitude and performs data preprocessing and feature extraction.  
Figure 6: Architecture of the three-dimensional CNN.  
During the experiment, six kinds of dynamic gestures, shown in Figure 8, were selected as the test gestures. The experimenter was required to complete the gestures in Figure 9 in different direction, positions and environments. The experiment was carried out in a laboratory, corridor and open square, two indoor and one outdoor. Each experimental environment is equipped with different equipment deployment and the location and direction of the test object. As shown in Figure 9, ABCDE represents five directions, and the red rectangular box represents six positions. The first, on the left of Figure 10 is the corridor plan, which is 3.3 m high and 2.5 m wide. The experiment was conducted at 6:00 a.m. without interference from others. The second experimental environment is the laboratory, which has many sundries, such as desks, bookshelves, office chairs and computers. The size is 5 m and 6 m. The first, on the right, is a relatively open outdoor experimental environment. The same experimental time was 6 a.m.  
Figure 8: Experimental gestures.  
Figure 7: IWR1642.  
Figure 9: Device deployment instance.  
In these three experimental environments (Figure 10), five experimenters tested the six gestures in Figure 8 in different positions and directions. Collect data from different environments, positions and directions. Then verify the accuracy of cross-scene gesture recognition after training. The experiment is to verify the gesture recognition effect in different scene domains, so it only needs to determine an influencing factor, train other factors and then take the data under this factor as the verification data set to verify the recognition results. If it is necessary to verify whether the gestures in the corridor scene in Figure 10 can be recognized in Figure 10 or Figure 10, the data in Figure 10 need to be trained and then the data in Figure 10 or Figure 10 should be used as a verification set for verification to obtain the recognition accuracy of Figure 10 in the whole scene domain. Similarly, the recognition accuracy of Figure 10 or Figure 10 can be obtained. The same method can be used to verify the verification results to verify the gesture recognition effect in different positions, directions and people. This way, the recognition rate of different scene domains can be obtained. The feasibility of cross-scene gesture recognition is further demonstrated.