An Integrated Human Stress Detection Sensor Using Supervised Algorithms

This paper adopts a holistic approach to stress detection issues in software and hardware phases and aims to develop and evaluate a specific low-power and low-cost sensor using physiological signals. First, a stress detection model is presented using a public data set, where four types of signals, temperature, respiration, electrocardiogram (ECG), and electrodermal activity (EDA), are processed to extract 65 features. Using Kruskal-Wallis analysis, it is shown that 43 out of 65 features demonstrate a significant difference between stress and relaxed states. K nearest neighbor (KNN) algorithm is implemented to distinguish these states, which yields a classification accuracy of 96.0 ± 2.4%. It is concluded that ECG and EDA signals are sufficient to detect stress with high accuracy while the system has fewer sensors and consumes lower power. Second, a novel sensor is developed to collect ECG and EDA signals from eighteen healthy participants aged 16 to 40 years. They were exposed to stress during an arithmetic mental task and Stroop Color-Word Test. This sensor has a battery life of 70 hours and can detect stress with 94.4 ± 2.5% accuracy. The proposed software and hardware can potentially be used in a wearable device for continuous stress detection.