Research on the application of biosensor technology in teacher psychological monitoring and intervention
Abstract
Teachers’ mental health and general well-being have been negatively impacted in recent years by the increasing stress they experienced as a result of several difficulties in both their personal and professional lives. Teachers’ psychological stress is a crucial area for intervention since it results in burnout, decreased teaching effectiveness, and other health problems. However, there is still an abundance of research on the application of innovative technologies to track and manage teachers’ mental health. This research suggests using deep learning (DL) techniques like the Intelligent Bottlenose Dolphin-Inspired Feed Forward Neural Networks based Teacher Psychological Monitoring and Intervention Model (IDBI-FFNN-TPMIM) combined with biosensor technologies. This model offers a novel method for determining mental stress levels, identifying early indicators of burnout, and classifying emotional states as neutral, negative, or positive using biosensors like EEG and biomechanical data. Using feature extraction approaches, the model properly depicts the physical and emotional states of teachers, allowing for automatic classification and feedback for prompt interventions. According to experimental data, Biosensor-based IDBI-FFNN-TPMIM results are F1-score at 91.1%, accuracy at 93.7%, recall at 91.5%, and precision at 92.3%. While performing well in psychological monitoring and emotion recognition while achieving high prediction accuracy. These findings demonstrate how biosensor technology is employ to improve overall well-being and strengthen programs for teachers’ mental health care.
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