Machine learning approach for stress detection using wireless physical activity tracker

Abstract

Stress is a psychological condition that reduces the quality of sleep and affects every facet of life. This paper provides an effective method for the cognitive stress level detection by using data provided from a physical activity tracker device Fitbit. The main motive of this system is to use machine learning approach in stress detection using sensor technology. Individually, the effect of each stressor is evaluated using logistic regression and then combined model is built and assessed using variants of ordinal logistic regression models like logit, probit and complementary log-log. This system is experimented and evaluated in a real time environment by taking data from adults working in IT and other sectors in India. The novelty of this work lies in the fact that stress detection system should be less invasive as possible for the users.