Recent Advances in Contactless Sensing Technologies for Mental Health Monitoring

The process of monitoring mental health has relied on methods such as invasive sensing and self-reporting. The use of these methods has been limited because of the invasiveness of sensing devices, or the subjective nature of patients’ responses. Recent research focuses on the contactless sensing methods used to objectively monitor mental health issues. These methods allow continuous collection of real-time data in a non-disruptive manner. Machine learning methods are then applied to the sensed data to predict information such as physical activity, gestures, and heart rate. This information can be then used to assess mental health issues such as depression, stress, anxiety, among others. This paper presents a comprehensive review of contactless sensing methods for mental health monitoring. It investigates the published research that focuses on contactless sensing methods to predict mental health condition. Moreover, this review categorizes the applications of contactless sensing methods into detection, recognition, and monitoring of vital signs. Furthermore, a comparison of recent studies on contactless sensing methods is presented, which shows the effectiveness and reliability of these methods. This study also highlights the existing challenges in contactless sensing methods and provides future research directions to mitigate these challenges.