# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 4 Experimental Results
The cell located at row \(i\) and column \(j\) of the matrix represents the percentage in which Gesture \(i\) is classified as Gesture \(j\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{Q_{1}}\) & \(\mathbf{Q_{2}}\) & \(\mathbf{Q_{3}}\) & \(\mathbf{Q_{4}}\) & \(\mathbf{Q_{5}}\) \\ \hline Proposed & 100.00 & 99.25 & 100.00 & 99.55 & 99.47 \\ SVM [6] & 90.38 & 87.66 & 86.06 & 73.76 & 89.42 \\ LSTM [7] & 92.89 & 99.25 & 95.82 & 100.00 & 99.47 \\ Bi-LSTM [17] & 87.87 & 98.49 & 100.00 & 99.55 & 99.47 \\ CNN [18] & 94.14 & 98.74 & 98.95 & 99.55 & 99.47 \\ Residual & 89.12 & 98.99 & 97.56 & 100.00 & 99.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracies (in percentage) of various algorithms, where \(Q_{i}\) is the classification accuracy of gesture class \(i\). We define \(Q_{i}\) as the number of gestures in class \(i\) that are correctly classified divided by the total number of gestures in class \(i\). corresponds to the misclassification of one foreground gesture to another foreground gesture. A deletion implies a misdetection of one foreground gesture. An insertion would be the results of the false detection of a background gesture as a foreground one, or the multiple detections of a single foreground gesture. Let \(E\) be the edit distance between two gesture sequences: one is the ground truth sequence, and the other is its prediction by the proposed algorithm. Furthermore, let \(U\) be the length of the ground truth of the gesture sequence (in number of gestures). We then defined the edit distance accuracy (EDA) as  
\[\text{EDA}=1-(E/U). \tag{23}\]  
Based on the definition, the EDA with highest accuracy is EDA = 1.0. As an example, consider a gesture sequence \(S=\{\text{Gesture 2, Gesture 4, Gesture 1}\}\). After the gesture detection and classification, suppose the outcome is \(S_{1}=\{\text{Gesture 2, Gesture 3, Gesture 5, Gesture 1}\}\). Because \(S\) actually contains three gestures, the length of the ground truth is \(U=3\). There exists one displacement and one insertion between \(S\) and \(S_{1}\). The edit distance is \(E=2\). From Equation (23), the EDA is \(1/3\). Figure 9 shows the average EDA of the proposed algorithm for various threshold values for detection \(T\). The average EDA was measured on the gesture sequences in the testing set. For comparison purpose, the average EDA of the proposed algorithm trained without regularization was also considered in Figure 9. Recall that the regularization was imposed by including the network branch for classification scores in the training process. Nevertheless, the branch and the scores were not used for inference. Therefore, it would be possible to remove from the training the network branch used for producing classification scores. However, it can be observed from Figure 9 that regularization was beneficial for improving the network performance. In fact, it outperformed its counterpart without regularization for all the thresholds considered in this experiment. In particular, when \(T=0.5\), the EDA values of the proposed algorithm with and without regularization were 0.879 and 0.821, respectively. An improvement in EDA by 0.058 was observed. These results justify the employment of regularization for the proposed algorithm. Another advantage is that the proposed algorithm may not be sensitive to the selection of thresholds. It can be observed from Figure 9 that the average EDA of the proposed algorithm is higher than 0.8 for \(T\) in the range of 0.4 to 0.8. The robustness of the proposed algorithm would be beneficial for providing a reliable performance for gesture detection and classification without the requirement for an elaborate search on the threshold. Although the experiments considered above were based on an inference procedure with stride size \(\Delta=1\), larger stride sizes can also be considered at the expense of a lower  
Figure 9: The average EDA of the proposed algorithm with and without regularization.