INVISIBLE DISABILITY AND ROUTINE WELLNESS TRACKER

Computer Science and Engineering

Invisible mental health conditions such as Attention Deficit Hyperactivity Disorder (ADHD) and depression often remain unnoticed because their symptoms are not physically visible and are frequently misunderstood or ignored. The absence of early identification and consistent monitoring can negatively affect an individual’s productivity, emotional stability, social interactions, and overall quality of life. Many people struggle with these challenges without realizing the underlying causes, which can delay appropriate support and intervention. The Invisible Disability and Routine Wellness Tracker is designed as an intelligent web-based platform that promotes early awareness, routine management, and access to professional consultation for individuals experiencing such conditions. The system enables users to securely register and record important lifestyle and behavioral information, including daily habits, mood variations, attention span, sleep cycle, productivity levels, and other physical or emotional indicators. This information helps create a continuous record of user behavior and wellness patterns. Using the collected data, the platform performs pattern analysis to generate an ADHD likelihood score and determine a depression severity level, which is categorized as low, medium, or high. Based on these results, the system offers personalized habit tracking, routine reminders, and productivity suggestions to help users improve concentration, manage daily tasks effectively, and maintain a healthier routine. In addition to self-monitoring features, the platform integrates a consultation module that allows users to schedule appointments with medical professionals for expert guidance and support. The system also includes an administrative module responsible for managing user data, updating questionnaires, ensuring privacy and data security, and monitoring the accuracy and performance of predictive models. By combining behavioral tracking, predictive analysis, and healthcare assistance in a single platform, the proposed system supports early recognition of potential mental health concerns and encourages individuals to adopt healthier routines for improved long-term well-being.

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