## 4 Experimental Results and Discussion
### Results and Discussion  
#### 4.3.1 Gesture Classification Using k-NN Algorithm  
In this study, a k-NN algorithm was used for gesture classification. Since the k-NN algorithm has the possibility to exhibit different levels of performance depending on the \(k\) value, it was necessary to confirm whether the experimental results were affected by the \(k\) value of k-NN. For this, the experimental results for when the \(k\) value was 3, 5, and 7 were compared. First, to confirm whether each subject's prediction time was affected by the \(k\) value, a one-way ANOVA was performed for the average prediction time, with respect to the  
Figure 9: Task time of each subject measured during the experiment. subject, when the \(k\) value was 3, 5, and 7. Table 1 shows the results of the normality test to perform one-way ANOVA. As a result of the normality test, as shown in Table 1, the significance level (red boxes in Table 1) of both the Kolmogorov-Smirnov and the Shapiro-Wilk test was greater than 0.05, so prediction time data satisfy the normal distribution. Next, the test result for equality of variance in consideration of the post hoc analysis of one-way ANOVA is shown in Table 2.  
As a result of the test for equality of variance, as shown in Table 2, the significance level (red box in Table 2) of Levene was greater than 0.05, so the equality of variance was confirmed. Thus, Table 3 shows the results of one-way ANOVA. As a result of the one-way ANOVA, as shown in Table 3, the significance level (red box in Table 3) was greater than 0.05, so the null hypothesis of one-way ANOVA was adopted. Based on this, even if the \(k\) value in k-NN was different, as 3, 5, and 7, respectively, it could be seen that there was no significant difference in the average prediction time for each subject. That is, it was confirmed that even if the \(k\) values of k-NN were different, there was no significant effect on the prediction time measured for each subject. Next, to confirm whether each subject's prediction success rate for grab gestures was affected by the \(k\) value, the one-way ANOVA was performed for the prediction success rate of grab gestures, with respect to subject, when the \(k\) value was 3, 5, and 7. Table 4 shows the results of the normality test to perform one-way ANOVA. As a result of the two-way ANOVA, as shown in Table 3, the significance level (red box in Table 3) was greater than 0.05, so the null hypothesis of one-way ANOVA was adopted. Based on this, even if the \(k\) value in k-NN was different, as 3, 5, and 7, respectively, it could be seen that there was no significant difference in the average prediction time for each subject. That is, it was confirmed that even if the \(k\) values of k-NN were different, there was no significant effect on the prediction time measured for each subject  
As a result of the normality test, as shown in Table 4, the significance level (red boxes in Table 4) of both the Kolmogorov-Smirnov and the Shapiro-Wilk test was greater than 0.05, so the prediction success rate of the grab gesture data satisfies the normal distribution. Next, the result of the test for equality of variance in consideration of the post hoc analysis of one-way ANOVA is shown in Table 5.  
As a result of the test for equality of variance (prediction success rate of grab gestures for each subject). As a result of the test for equality of variance, as shown in Table 5, the significance level (red box in Table 5) of Levene was greater than 0.05, so the equality of variance was confirmed. Thus, Table 6 shows the results of one-way ANOVA. As a result of one-way ANOVA, as shown in Table 6, the significance level (red box in Table 6) was greater than 0.05, so the null hypothesis of one-way ANOVA was adopted. Based on this, even if the \(k\) value in k-NN was different, as 3, 5, and 7, respectively, it could be seen that there was no significant difference in the average prediction success rate of the grab gestures for each subject.