Predictive Bio-Sensing: Integrating Ambient Computer Vision and Behavior Modeling for Early Mental Health Intervention in Urban Populations

This article proposes a new concept of passive behavioral monitoring in cities to locate an individual who is experiencing a decline in mental health at an early stage. The proposed system takes a hybrid method to leverage privacy-preservation in computer vision technology and combines it with behavioral drift to capture nuances in movement patterns, social interaction, and other non-verbal cues, which can be signs that a person is not mentally well. In contrast to conventional solutions that were based on active self-reporting or biometric intrusion, this framework reaches semi-public and public places without the collection of any personally identifiable data. The system uses temporal aggregations of anonymized behavioral markers in order to identify meaningful patterns, but the privacy of an individual is not impacted. The system notifies the specified mental health professionals when the predefined risk thresholds are exceeded, who could take appropriate actions. The architecture also includes various technical measures such as data minimization, the requirement of aggregation, temporal separation, minimization in data retention, and opt-out controls to make the privacy protection very robust. The applications may include urban planning, workplace wellness, and educational environments, and the information might have profound implications on the proactive management of mental health across human populations, which are becoming more and more urbanized and have to deal with an increasing number of psychological issues.