Psychological Health Assessment and Intervention System for Chemical Engineering Students Based on Emotional Computing Technology

Abstract

Emotional computing technology is revolutionizing health assessment by integrating emotional and psychological factors into traditional diagnostic processes. This innovative approach utilizes advanced algorithms and sensor technologies to analyze individuals' emotional states and mental well-being, providing valuable insights into their overall health. By detecting subtle changes in facial expressions, voice patterns, and physiological signals, emotional computing technology can assess stress levels, mood fluctuations, and mental health conditions with greater accuracy and sensitivity. This paper presents a novel Psychological Health Assessment and Intervention System tailored for chemical engineering students, leveraging Emotional Computing Technology enhanced by Cartesian Feature Weighted Emotional Classification (CFWEC). The system aims to assess students' psychological well-being by analyzing emotional cues extracted from various sources such as facial expressions, voice patterns, and text interactions. Through simulated experiments and empirical validations, the efficacy of CFWEC-enhanced emotional computing technology is evaluated in predicting and intervening in psychological health issues among chemical engineering students. Results demonstrate significant improvements in accuracy and sensitivity compared to traditional assessment methods. For instance, the CFWEC model achieved an average accuracy rate of 85% in identifying students at risk of psychological distress, enabling timely interventions. Additionally, the system provides personalized recommendations and interventions based on individual emotional profiles, leading to improved mental health outcomes. These findings underscore the potential of Emotional Computing Technology with CFWEC in promoting psychological health and well-being among chemical engineering students.