Stress Detection from Sensor Data using Machine Learning Algorithms

Today, psychological stress is a huge problem. Mental stress, such as anxiety, overthinking, melancholy, and emotional imbalance, was common during pandemics. Pandemics are mostly caused by a mismatch between labour market demands and slowing economic development. The paper’s main goal is to use machine learning algorithms to estimating the levels of stress and that can be detected by grouping together many measurements such as pulse rate, body temperature, heart rate, and systolic blood oxygen saturation (spo2). When a person is under stress, their bio-signals, such as thermally, electrical, impedance, acoustic, and optical, change noticeably. These bio-signals can be used to measure stress levels. Accelerometer, body temperature, respiration, blood volume pulse electrocardiogram, (BVP), electro dermal activity, and other sensor modalities (EDA). In Machine Learning Classification methods such as Kernel Support Vector Machine, K-Nearest Neighbour, AdaBoost, Random Forest and Decision Tree methods used to evaluate and compare the classifications. The Random Forest model beat the other approaches with F1-scores of 93.77 and 70.03 for classification model and three-class classification, respectively.