# Continuous Recognition of Teachers' Hand Signals for Students with Attention Deficits
## 4 Discussion  
Our proposed method differs from those described in [12; 13; 14] in the following aspects. While they focus on binary classification tasks using the single frame in the video sequence, our algorithm is tailored for continuous recognition with classes designated as _Pointing to left, Pointing to right_, and _Non-pointing_. In addition, our method considers avoiding unintended arm movements and triggers signal recognition only when a signal transition is maintained for a sufficient time interval. Table 3 shows a comparison of the proposed methods with those in [12; 13; 14]. The method described in [12] is able to recognize situations where the teacher extends his/her arm and gestures towards the whiteboard. However, our approach allows teachers to use a telescoping pointer to guide attention. In [12], the accuracy of pointing gesture recognition at the first stage is 90%. By combining whiteboard detection, the final precision is 89.2% and the recall is 88.9% for instructional pointing gestures. In [13], the final accuracy exceeds 90% after the training process. In [14], the accuracy depends on the training and testing methods, which combine the different available datasets, ranging between 54% and 78%. The available training and testing datasets included acted recordings and real-class recordings; in both their semi-automatic classification versions and manual classification versions. While [12] and [14] rely on OpenPose, we utilize Mediapipe BlazePose with faster processing speed for pose information capture. Our method achieves competent performance with an accuracy of 88.31%, a sensitivity (recall) of 91.03%, and a precision of 86.32%, potentially coupled with expedited implementation speed compared to those machine learning or deep learning models in previous works, which can be attributed to our simple algorithm. The methods provided in [39; 40] use continuous wave radar for real-time hand gesture recognition and achieve high accuracy rates. However, our choice of a video-based approach is motivated by considerations of accessibility, ease of implementation, and practical utility in inclusive education environments where resources and infrastructure may be limited. Given that most teachers have smart phones, this video-based method becomes highly convenient and widely accessible. The fundamental possibility of recognizing audio-visual speech and gestures using sensors on mobile devices is demonstrated in [41]. Studies have shown that wearable devices can effectively improve attention in educational settings through smart watch vibrations or visual stimuli from smart glasses [42; 43; 44; 45; 46]. Therefore, future implementations of our recognition algorithm can be embedded in wearable devices such as smart watches or smart glasses. The benefit of sending recognized signals to students' smart watches or smart glasses is to provide real-time feedback and assistance. Compared to smart phones, these devices provide attention cues to students in a less distracting manner. For students with attention deficits, this can help them stay focused, ameliorate hyperactive behaviors [43], and improve cognition [46]. Using wearable devices can make these students \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Classification Task** & **Classes** &
\begin{tabular}{c} **Keypoint** \\ **Extraction** \\ \end{tabular} & **Classification** & **Accuracy** \\ \hline
[12] & Pointing or not & 2 & OpenPose &
\begin{tabular}{c} Non-linear \\ neural network \\ \end{tabular} & 90\% \\ \hline
[13] & Pointing or not & 2 & \
\begin{tabular}{c} Convolutional neural \\ network \\ \end{tabular} &
\begin{tabular}{c} Convolutional \\ neural \\ network \\ \end{tabular} & over 90\% \\ \hline
[14] & Gesticulating or not & 2 & OpenPose & \
\begin{tabular}{c} Machine \\ learning \\ \end{tabular} & 54–78\% \\ \hline Proposed method &
\begin{tabular}{c} Pointing to left \\ Pointing to right \\ Non-pointing \\ \end{tabular} & 3 & MediaPipe & Simple rules & 88.31\% \\ \hline \hline \end{tabular}
\end{table}