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Continuous Recognition of Teachers’ Hand Signals for Students with Attention Deficits
oleh: Ivane Delos Santos Chen, Chieh-Ming Yang, Shang-Shu Wu, Chih-Kang Yang, Mei-Juan Chen, Chia-Hung Yeh, Yuan-Hong Lin
Format: | Article |
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Diterbitkan: | MDPI AG 2024-07-01 |
Deskripsi
In the era of inclusive education, students with attention deficits are integrated into the general classroom. To ensure a seamless transition of students’ focus towards the teacher’s instruction throughout the course and to align with the teaching pace, this paper proposes a continuous recognition algorithm for capturing teachers’ dynamic gesture signals. This algorithm aims to offer instructional attention cues for students with attention deficits. According to the body landmarks of the teacher’s skeleton by using vision and machine learning-based MediaPipe BlazePose, the proposed method uses simple rules to detect the teacher’s hand signals dynamically and provides three kinds of attention cues (<i>Pointing to left</i>, <i>Pointing to right</i>, and <i>Non-pointing</i>) during the class. Experimental results show the average accuracy, sensitivity, specificity, precision, and F<sub>1</sub> score achieved 88.31%, 91.03%, 93.99%, 86.32%, and 88.03%, respectively. By analyzing non-verbal behavior, our method of competent performance can replace verbal reminders from the teacher and be helpful for students with attention deficits in inclusive education.