An Individual-Oriented Algorithm for Stress Detection in Wearable Sensor Measurements

Abstract

Accurately measuring a person's level of stress can have a wide variety of impacts, not only on human health, but also on the perceived feeling of safety when going after daily habits, such as walking, cycling, or driving from one place to another. While there is a vast amount of research done on stress and the related physiological responses of the human body, there is no go-to method when it comes to measuring acute stress in a live setting. This work proposes an advancement of the rule-based stress detection algorithm proposed by Kyriakou et al., to identify moments of stress (MOS) more reliably, through an adaptation, and individualization of the rules proposed in the original paper. The proposed algorithm leverages electrodermal activity (EDA) and skin temperature (ST), both recorded by the Empatica E4 wristband, for the assessment of an individual's stress when exposed to an audible stimulus. The algorithm achieves an average recall of 81.31%, with a precision of 46.23%, and an accuracy of 92.74%, measured on 16 test subjects. The tradeoff between precision and recall can be controlled by adjusting the MOS threshold that needs to be reached for an MOS to be detected.