Stress Detection Using Machine Learning Techniques in IoT Environment

Abstract

Employment of various sensors used in IoT can help create a sustainable urban life. Rapid advancements in IoT have made human existence smarter. Smartness may be associated to office, home, networks, energy consumption, agriculture, education, retail, and even healthcare. Smart health management addresses its populations, such as tracking routine activities, obesity, nutrition intake, heart rate, glucose level, oxygen level, body temperature, or even stress level monitoring. Stress is a condition which is being faced by people irrespective of their age, gender, or profession. But its identification at an early stage can help in preventing the consequences. This work presents nine machine learning techniques for identifying stress using the SWELL dataset. Hence, automated classifiers were utilised to predict working circumstances and stress-related mental states and were compared for accuracy for three stress conditions (no stress, interruption, and time pressure).