This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along …