Research on the Application of Intelligent Sensors in Teaching Interactive Environment

This paper proposes an innovative behavior perception and emotion analysis algorithm combining deep learning and reinforcement learning, aiming to achieve realtime perception of students' behavior and emotional state through intelligent sensors, and provide personalized teaching feedback based on this perception information. Multimodal sensors (such as cameras, sound sensors, motion sensors, etc.) are used to capture students' facial expressions, body changes, voice emotions and other data. The deep learning model is used to analyze the students' learning status, and the teaching strategy is dynamically adjusted in combination with the reinforcement learning algorithm to optimize the classroom interaction effect and student participation. The experimental results show that compared with traditional teaching methods, the interactive teaching system based on intelligent sensors performs well in multiple indicators. Specific data analysis shows that the system can perceive students' emotional changes in real time, with an accuracy improvement of more than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 0 \%}$</tex>, a 15 % increase in students' classroom participation, and an 18 % improvement in learning effect. In addition, the system can also adjust the teaching content and methods according to students' real-time feedback to improve personalized learning effects. The research in this paper not only provides new solutions for intelligent education, but also lays a theoretical foundation for the development of future educational technology, and has broad application prospects and practical significance.