Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors

Abstract

This work presents an approach for detecting pilgrims' stress levels using their nightly sleep patterns and identifying the most relevant sleep parameters for stress detection. We collected data from 10 participants, who wore two wearable devices, a chest strap and a wristband, during the 2013 Hajj pilgrimage. To build and evaluate different classification models we use bio-physiological measures such as ECG/HRV, respiration, body temperature and GSR data, and physical parameters including upper body posture sensors and accelerometers on the body and arms. The best classifier is capable of differentiating between low, moderate, and high-perceived stress with an accuracy of 73 %. Both physical and bio-physiological feature modalities show similar individual performance. Sleep duration and upper body activity on the one side and ECG/HRV features on the other are the most important sleep parameters.

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