Authors - Grishma Bobhate, Pawan Bhaladhare Abstract - Internet of behavior is a primitive approach to study behavior analysis and predictive learning system to understand user experience and interpret their psychological patterns for betterment of the society. Basically, Internet of Things and Internet of Behavior are closely related to each other and can offer different techniques in various areas for developing technology and significant applications. Major psychological disorder, or depression, is a common but fatal neurological condition that has a disruptive impact on feelings, actions, and ways of seeing actuality. Various Machine learning algorithm have been implemented to detect depression through fusion modalities applied on different parameters such as visual, textual and gaze movement. To ensure preventive measure and provide ethical frameworks, this study aims to identify Internet of behavior technology that can have crucial importance in the comprehensive study of depression detection in healthcare sectors. With the assessment in health monitoring systems, the main objective is to explore and analyze the strategies in the Internet of behavior technology for understanding patient behavior and mental health to detect depression and mood. Various challenges towards Internet of behaviors has discussed. In order to ensure the reliability of the system, it also explores the different machine learning and deep learning approaches to determine depression with the performance validation. This will help to assist medical personnel in acquiring details and evaluating the actions of patients for an effective regimen of treatments. This study outlines the strategies to adopt the behavioral analysis for effective learning of depression detection model.
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