Research on enhancing the accessibility of psychological health services by using media communication technology and biomechanical biosensors
Abstract
Access to mental health services remains a global challenge, particularly for marginalized groups. This research endeavors to enhance the accessibility of mental health services by integrating media communication technology with biomechanical biosensors, including electrodermal activity sensors and heart rate monitors. The proposed approach leverages mobile communication platforms and wearable biosensors for real-time biomechanical parameter monitoring (including heart rate, blood pressure, respiratory rate, body temperature, and galvanic skin response, etc.) and remote interventions. Judge the impact on the brain and neuroendocrine system through the changes in biomechanical indicators, and use this as a basis for judging mental health. The objective is to develop a telehealth model that merges bio-data-driven alerts with communication tools to deliver prompt psychological support. This study underscores the deficiencies of traditional health systems in ensuring comprehensive mental health monitoring and emphasizes the potential of media communication technologies as scalable and accessible tools for early interventions in underserved areas, and also emphasizes the relationship between the physiological indicators measured by biosensors and the biomechanical mechanisms of mental health. Despite the existence of online methods for detecting mental health issues, early detection remains problematic. This research presents a framework for integrating pre-processed biosignal data with user-generated content to facilitate proactive monitoring. To address the limitations of conventional classifiers, the study introduces a Fitness-Dependent Optimizer-tuned Upgraded Decision Tree (FDO-UDT) model, which enhances the early identification of at-risk individuals using personalized thresholds and real-time event detection based on biomechanical data, it is helpful to provide an early warning before the clinical symptoms of mental health problems occur. The results indicate that automated alerts triggered by biomechanical sensor thresholds improve responsiveness and engagement, ensuring timely interventions for those in need. The FDO-UDT model achieves performance metrics of 90.21% accuracy, 98.01% recall rate, and 86.38% precision, outperforming traditional methods. The study concludes that the integration of media communication technologies with biomechanical sensors offers scalable solutions to improve the delivery of mental health services, especially for rural and underserved populations.
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