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.
Index Terms—Physical activity tracker, sleep pattern, working hours, heart rate, smartphone sensor.
B. Padmaja is with the Computer Science and Engineering Department, Institute of Aeronautical Engineering College, Hyderabad, Telangana, India (e-mail: email@example.com).
V. V. Rama Prasad a is with the Computer Science and Engineering Department, Sree Vidyanikethan Engineering College, Tirupati, India (e-mail: firstname.lastname@example.org).
K. V. N. Sunitha is with the Computer Science and Engineering Department, BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India (e-mail: email@example.com).
Cite: B. Padmaja, V. V. Rama Prasad, and K. V. N. Sunitha, "Machine Learning Approach for Stress Detection using Wireless Physical Activity Tracker," International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 33-38, 2018.