Design And Implementation Of Student Learning Behavior Recognition System Based On Key Points Detection Of Bones

Classroom teaching has always been a place where teachers teach knowledge,students learn knowledge,and it is a space for interaction between teachers,students and students.It is the main channel for teachers to guide students to develop and explore knowledge.Therefore,classroom teaching is the main battlefield for teachers to improve the quality of classroom teaching and achieve effective teaching.At the same time,classroom teaching is also a cultivation field for students to improve themselves and improve themselves.However,there are a large number of students in the classroom today.Teachers can't observe the learning behavior of each student in the teaching process.At the same time,many students can't consciously control their learning behaviors in class,often there is no concentration.The situation of listening to the lectures,such a classroom without feedback on teaching quality and efficiency,not only consumes the teaching energy of the teachers,but also the students do not get the learning results,which is an urgent problem to be solved in the current education field.In view of the above problems,relevant research is to evaluate the behavior of students in the classroom by using artificial intelligence technology to acquire and analyze the facial expressions and voice data of students in the classroom.However,in the classroom application scenario,if the students are in the classroom application scenario,Under the circumstances of bowing or turning,the system will not be able to detect and recognize the facial behavior of students,and there are certain application limitations.Analysis of the above problems This paper uses computer vision technology to timely acquire and analyze the learning behaviors of students in the classroom.Compared with facial behavior detection,the physical behavior of students can reflect the students' listening status to a certain extent and is more suitable for classrooms.Application scenario.This paper first studies the key point detection method of bones,introduces the network architecture of OpenPose model,describes the training process of the two branches of neural network,and explains the calculation method of confidence and affinity.The method and principle of key point detection are described.The OpenPose bone key detection model in the bottom-up bone key detection method is used to detect the key points of each student's learning behavior in the image.Then,after the key point relationship analysis is carried out for the five kinds of learning behaviors that need to be identified in this paper,the direct key coordinate method of bone key points is proposed to identify the two actions of raising hands and stretching.The traits are extracted by using the skeleton key point feature extraction method for the three behaviors of squatting,playing mobile phone and writing.The extracted feature vectors are classified and trained by the support vector machine to generate the model.Finally,based on the above research content,the learning behavior evaluation system is designed and implemented.The system is analyzed in detail,and the system is divided into four modules: data training module,behavior acquisition module,behavior recognition module and data analysis module.Each module realizes different functions,and finally realizes the realization of the system and The results were presented,along with an explanation of the shortcomings in this paper and the outlook for future research.