## 4 Experiments and Results
### Results of the HMT Gesture Recognition
#### 4.2.3 Results of the HMT Gesture Recognition  
After obtaining the effective velocity data of the corresponding gestures, 1000 gestures were classified with three different cross-validation methods, based on the distance between the velocity data of the corresponding gestures calculated by a DTW algorithm. In order to meet the data requirements of the DTW algorithm, the original velocity data were normalized and resampled with 30 sampling points by linear interpolation before classification. The three cross-validation methods are introduced as follows: (1) leave-one-subject-out (LOSO) cross-validation is a method that refers to selecting the sample data of one subject as the test set and the sample data of the other subjects as the training set; (2) leave-other-subject-out (LPO) cross-validation refers to selecting the sample data of one subject as the training set and the sample data of the other subjects as the test set; (3) leave-one-group-out within one subject (LOOWS) cross-validation refers to selecting one group of samples from one subject as the test set and the other groups of samples of this subject as the training set. The types of the test gestures were determined according to the shortest distance to the training set based on the DTW algorithm, and the most types that the test gesture was determined to be were taken as the recognition results. Finally, the accuracies of gesture recognition using different cross-validation methods are shown in Table 4. The mean recognition accuracy with the LOSO cross-validation was up to 97.34%, which verifies the system's performance in front of an unknown subject. The LPO cross-validation achieved a mean accuracy of 96.55%, which is lower than LOSO, and reflects the variations between different subjects and the diversity of our data. The LOOWS method achieved a mean accuracy of 98%, which is higher than LOSO, and indicates that the user can easily add their characteristic gestures to the system, and the gestures can be recognized efficiently. As shown in Table 4, the HMT gesture recognition accuracies in the standing condition are slightly higher than those in the sitting condition. Besides the random influence of the external environment, the features of the gesture in the standing condition are more evident than those in the sitting condition since the action space in the standing condition is much larger than that in the sitting condition. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Posture**} & \multicolumn{4}{c}{**Sitting Condition**} & \multicolumn{4}{c}{**Standing Condition**} & \multicolumn{4}{c}{**Mean**} \\ \hline \multicolumn{2}{c}{**Subject**} & **1** & **2** & **3** & **4** & **5** & **Mean** & **1** & **2** & **3** & **4** & **5** & **Mean** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **Recognition** \\ **Accuracy** \\ **(\%)** \\ \end{tabular} } & **LOSO** & 97.33 & 96.78 & 93.68 & 97.89 & 95.67 & 96.27 & 99.67 & 98.17 & 96.44 & 99.33 & 98.48 & 98.42 & 97.34 \\  & LPO & 96.56 & 97.56 & 93.78 & 97.33 & 94.67 & 95.98 & 99.37 & 97.56 & 94.44 & 98.47 & 97.56 & 97.47 & 96.55 \\  & LOOWS & 97.67 & 90.00 & 100 & 100 & 100 & 97.53 & 98.33 & 100 & 94.00 & 100 & 100 & 98.47 & 98.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gesture recognition accuracies using three cross-validation methods with five subjects. LOSO: leave-one-subject-out, LPO: leave-other-subject-out, LOOWS: leave-one-group-out within one subject. Figure 9: Velocity curves sample of the 10 predefined HMT gestures. In order to cater for the interactive application of the two interaction modes with the nursing-care assistant robot, the gesture velocity data collected in the standing and sitting conditions based on LOSO cross-validation are analyzed, respectively.