Stress Prediction and Detection in Internet of Things using Learning Methods

Stress is a mental condition experienced over an interval of time and has a negative impact on the health and further issues regarding nervous system and over stress leads to muscular related problems. As well known, stress is a key issue in this modern society and every sector of the society is under pressure and this affects the mental health of the social people. For the monitoring of stress there are many approaches. Mental negative health can be dealt by using several external sensor network systems and monitoring apps developed by techniques under machine learning, and the major steps are intended for the internet of things. In major issues, the stress can be detected by heartbeat readings, electrocardiogram (ECG), Pulse sensor and many other techniques. The article focuses on the impact of stress detection of a person with analysis of Internet of Things (IoT) sensed data along with machine learning algorithms. The physiological data can be detected by performing the stressor tests on the person, and they include arithmetic calculations. Some of those algorithms are Support Vector Machine (SVM), Logistic-regression, K-nearest neighbor, adaptive neuro-fuzzy inference system (ANFIS) decision-tree and so on. By analyzing the results from the literature, machine learning algorithms provides high detection accuracy of 95% with 90% F1 score and reduced prediction errors.