Abstract

Mental health issues, such as anxiety, depression, and other psychological disturbances, have become a significant public health concern globally. Identifying individuals at risk of these conditions early on can lead to timely interventions and improved outcomes. With the rise of technology and the increasing use of smartphones and wearable devices, there is an opportunity to develop an innovative system that can monitor emotional health in real-time, helping to detect potential psychological disturbances before they escalate. Traditionally, mental health assessments relied on self-reporting and periodic check-ins with mental health professionals. These approaches often had limitations, as individuals may not always accurately report their emotional state, and there could be significant delays between assessments. Additionally, access to mental health services was not always readily available, leading to potential delays in diagnosis and treatment. Therefore, the need for an innovative system for monitoring emotional health arises from the desire to overcome the limitations of traditional approaches. By leveraging technology, such as machine learning, natural language processing, we can create a continuous and unobtrusive monitoring system. Such a system could gather real-time data on an individual's emotional state, behavior, and physiological responses. Early detection of emotional disturbances can lead to timely intervention and support, improving the overall mental well-being of individuals and reducing the burden on mental health services. This innovative monitoring system has the potential to significantly improve mental health outcomes on a broader scale. It offers a proactive approach to emotional well-being, empowering individuals to take control of their mental health and providing an invaluable tool for mental health professionals in identifying and supporting those at risk of psychological disturbances

Description