Application of Non-contact Health Monitoring and Emotion Recognition Technology in Psychological Health Intervention

Abstract

Mental health issues have become increasingly prevalent in modern society, driving the advancement of emotion recognition technologies for psychological interventions. This study investigates non-contact health monitoring and emotion recognition technology for auxiliary detection, diagnosis, and treatment of mental disorders including depression, anxiety, and autism spectrum disorders. By analyzing physiological signals and behavioral data, this technology provides objective evidence for clinical decision-making. Physiological signals such as EEG and ECG are difficult to disguise, and deep learning algorithms significantly enhance emotion measurement accuracy. Research demonstrates that this approach optimizes mental health monitoring processes, supports clinical decisions, and enables simultaneous monitoring of multiple patients. The technology exhibits substantial clinical value and is expected to promote digital transformation and personalized healthcare services.