Performance Analysis and Detection of Stress using IoT Sensors

S. Srinivasulu Raju; T. Niranjan; P. Akhil Sai; S. K. Mohinuddin

Abstract

The objective of this paper is to review recent researches on several stress prediction and detection techniques and also to provide better streamline on various stress management techniques. Worldwide, people experience mental stress concerns with different reasons including family issues, financial issues, social issues and environmental issues. The adjustable stress can develop a person; however, the strong response or continuous stress becomes critical. Hence, prediction, detection and management of physical and mental stress become important research area. It is necessary to predict the stress level earlier to avoid the major chronic health disorders to an individual’s well-being. Nowadays, more research is going to find various stress detection techniques based on physiological measures such as Heart Rate Variability (HRV), Electromyogram (EMG), Electrocardiogram (ECG), Electroencephalogram (EEG), Skin Conductance (SC), Sleep Pattern, Galvanic Skin Response (GSR)and Skin Temperature. The behavior of physical and mental stress is studied with the help of emerging technology such as Internet of Things (IoT). To track and compute enormous data over a long period of time, cloud-based work and smart phone applications efficiently manage the identified stress measures. In this paper, various approaches of researchers are found for stress prediction, detection and management are reviewed. It suggests directions towards future scope of the recent research and interventions.