# FlickPose: A Hand Tracking-Based Text Input System for Mobile Users Wearing Smart Glasses
## 5 Experiments to Evaluate Text Input System
### Discussion  
This paper presents a novel text input method that integrates hand posing and key selection. We first designed the configurations and input functions for both the touch-screen and raycast systems. Then, we collected data to train a hand pose estimation model and developed the model accordingly. Finally, we conducted text input experiments to compare its performance and usability with the standard method. The results revealed several new insights. The raycast method resulted in the fewest deletions across all tasks, averaging about 33.5% fewer than the standard method. Unlike the touch-panel and standard methods, the raycast method involves pointing at a key and performing a pinch gesture to input it. This approach avoids touching untactile virtual keys and allows for better control over input timing, which likely contributed to its higher accuracy. Although the touch-panel method also requires touching keys, similar to the standard method, it showed about 13.5% fewer deletions. This may be due to the FlickPose key layout, which uses fewer and larger keys compared to the traditional QWERTY layout. Additionally, FlickPose reduces the number of key selections needed for entering Japanese words used in the tasks. These factors likely contributed to the lower deletion rate compared to the standard method. As for the number of consecutive deletions, the raycast method has the lowest overall value. This may be because the raycast method allows users to select keys by the direction of their hands alone, regardless of the distance from their hands to the UI panel, making it easier than the other two methods to keep their eyes near the character entry field, leading \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Standard** & **Touch-Panel** & **Raycast** \\ \hline Usability score & & & \\ average & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Usability score average. Figure 14: Distribution of responses to each question in questionnaire. to the early detection of incorrect input. In addition, as a common feature of all methods, the number of consecutive deletions in the hiragana + kanji task was the largest. This is likely due to the fact that when performing a kanji conversion, if a mistake is made in the conversion destination, it is necessary to delete the entire conversion point, which causes the continuous deletion. Next, considering the overall evaluation indices, the standard method was the fastest in terms of character input speed on average for all tasks, and the raycast method was the best for the two deletion-related evaluation indices. As mentioned earlier, the raycast method, which does not require finger touch, is superior in terms of input accuracy, but operations unique to the XR environment, such as rays and pinches, may have hindered subjects who had no experience using HMDs from learning the system. Here, since this study was designed to be used by mobile users and it was expected that the users are regular users of HMDs, the results for those with HMD experience were also important. Therefore, Table 11 shows the average values and the standard deviation for all tasks of text input speed, the number of deletions, and number of consecutive deletions for the four subjects who indicated that they had experience using the VR headset at least once a week. Table 11 also shows that the raycast method achieved the best performance across all evaluation metrics for frequent HMD users. While the touch-panel method still showed slower input speed and more deletions compared to the other two methods, the differences appeared smaller than those observed in the results for all participants. One advantage of FlickPose is that it does not rely on predicting the intended character or key. Previous studies have explored word prediction based on cursor trajectories (e.g., [PERSON] et al. (2018), [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2020)). PinchText, proposed by [PERSON] et al.