*1.1. Background Information on Students' Classroom Behavior*

With the rapid development, penetration, and integration of artificial intelligence technologies in various areas of society, intelligent digital-based education is progressively becoming a hot issue of substantive research [1,2]. Among the many educational research

**Citation:** Wang, H.; Gao, C.; Fu, H.; Ma, C.Z.-H.; Wang, Q.; He, Z.; Li, M. Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors. *Bioengineering* **2023**, *10*, 127. https://doi.org/10.3390/ bioengineering10020127

Academic Editor: Franz Konstantin Fuss

Received: 26 December 2022 Revised: 11 January 2023 Accepted: 12 January 2023 Published: 18 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

carriers, the intelligent education classroom scenarios are still the commonly adopted educational method [3], which has the outstanding advantages of direct feedback and extensive interaction between teachers and students [4].

Classroom scenarios present complexity and diversity according to different participants and instructional content. Research has shown that in classroom scenarios, students' classroom behavior is one of the most important factors influencing their academic performance [5]. Compared with high-achieving students, low-achieving students typically spend a significant amount of class time engaged in non-academic work or other academic work [6]. Therefore, investigating student classroom behavior has essential research implications and applicate values for enhancing student performance and promoting instructional strategies [7].

Specifically, the study of classroom behavior through detecting and identifying students' classroom behavior patterns can provide timely and stage-specific feedback on students' classroom performance. Effective statistical analysis of students' behavior patterns will assist students in effectively understanding their learning habits, timely correcting their poor classroom behavior, improving learning strategies, adjusting learning progress, and deepening their understanding and absorption of knowledge.

Furthermore, the analysis of students' classroom behavior is especially beneficial for students with special education needs (SEN) and developmental disabilities, such as attention deficit and hyperactivity disorder (ADHD) [8], autism spectrum disorder (ASD) [9], and learning disabilities [9,10]. Conducting classroom behavior analysis is crucial to improving these students' classroom performance and enhancing their classroom concentration. The percentage of school-aged children diagnosed with developmental disorders is increasing dramatically each year due to various environmental factors such as location, level of education, and medical care. In addition, the percentage of children with developmental disorders increased to 17.8% of all children (3–17 years old, the United States). The proportion is substantial, with approximately one in six children diagnosed with a disease [11]. Specifically, ADHD has the broadest range of effects on all developmental disorders and has the most significant prevalence among children. Characteristics of children with ADHD include inattention, hyperactivity, and impulsivity. Students with developmental disorders generally suffer from academic problems due to physical or psychological issues, and their classroom performance is difficult to self-control.

The study of classroom behaviors of students with developmental disabilities can be used to detect and identify their classroom behaviors automatically to a large extent. It can help them improve their self-awareness, enhance their concentration, and effectively achieve supplementary education without external interventions [12]. Auxiliary education based on non-artificial reminders can greatly relieve their learning pressure, ease learning difficulties and anxiety, increase knowledge and improve environmental adaptability, and promote a virtuous cycle of learning [13].

Finally, due to the need to build intelligent digital education platforms for schools and parents, the study of classroom behavior can further refine students' learning performance at school [14], optimize school teaching services, improve teaching strategies, and facilitate communication and exchange among multiple parties [15]. The intelligent digital platform is designed with students as the primary body and their classroom behaviors as the principal way of measuring their classroom status in order to enhance students' learning performance and optimize the teaching services of teachers. The perception and identification of students' classroom behaviors open the door to the development of an intelligent digital education platform.
