Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

Sensors

Niaz Chalabianloo¹  Cem Ersoy¹  Deniz Ekiz¹  YektaSaid Can¹ 

[1] Department of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey;

关键词: stress recognition;    machine learning;    wearable sensors;    smartwatch;    photoplethysmography;    electrodermal activity;    daily life psychophysiological data;    heart rate variability;   

DOI  :  10.3390/s19081849

来源: DOAJ

【 摘 要 】

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

【 授权许可】

Unknown