## 4 Experiments and Results  
### Dataset  
Before the experiment, the approval of the Ethics Committee of the 117 th Hospital of People's Liberation Army of China (PLA) has been obtained, and all of the subjects have signed a consent form. In this study, the performance of the HMT gesture recognition system is tested and verified with five subjects in two postures (sitting and standing); then, we conducted the application on the nursing-care assistant robot by controlling the robot's movement based on the predefined 10 gestures using the wrist-worn camera.  
In order to verify the performance of the gesture recognition system, five subjects of four males and one female, aged from 20-30 with a healthy and movable body, were included in the experiment. In the experiment, the test subjects performed the predefined 10 gestures continuously in one round as a gesture combination. Each subject repeated the gesture combination 10 times in both sitting conditions and standing conditions, respectively. In other words, 200 gesture samples were collected from each subject. Finally, 500 gesture samples were obtained in each condition, and a total of 1000 gesture samples were obtained. During the experiment, the participant put the wearable wrist-worn camera on  
Figure 8: Ten predefined gestures for the interaction between human and nursing-care assistant robot.  
their right wrist. Then, the camera was positioned to ensure a suitable proportion of the hand area in the image. After that, the participant performed the predefined 10 HMT gestures one by one to collect gesture data following the predefined rules. The participant performed the motion of fist bobbing firstly, and then made the single effective HMT gesture. After finishing a single gesture, the participant needed to maintain a motionless state for more than half a second. The test subjects were guided by the above rules and pretrained to perform the gesture combination once, which was not included as one of the experimental samples to be analyzed. After finishing all of the HMT gestures, the original video data of the gestures were collected and processed to extract the velocity data of the effective single HMT gesture. Then, a DTW algorithm was used for measuring the similarity of the different gestures, and three different methods of cross-validation were used to classify the gestures. The recognition accuracies based on different cross-validation were obtained for the performance verification. After the experiments for performance verification, we applied the HMT gesture recognition system to interact with the nursing-care assistant robot. Similarly, the participant put on the wearable camera to control the nursing-care assistant robot in its man-in-seat interaction mode and remote interaction mode, respectively, based on the predefined 10 gestures. What is different from the experiments in the application is that the HMT gesture recognition is in real time, which is based on a specified training set using a representative method of cross-validation for the classification. The data processing and algorithm verification that were involved in this experiment were all carried out in version 2018a of MATLAB installed on a computer configured with 8 G of memory and 2.6 Ghz CPU.