Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal machine-learning technique for acute stress episodes detection, by fusing the information careered in several biosignals and wearable sensors. Further-more, we investigate the contribution of each wearable sensor in stress detection and demonstrate the possibility of acute stress recognition using wearable devices. In particular, we acquire the physiological signals using the Shimmer3 ECG Unit and the Empatica E4 wristband. Our experimental evaluation shows that it is possible to detect acute stress episodes with an accuracy of 84.13%, for an unseen test set, using multi-modal machine-learning and sensor-fusion techniques.


Published in:
2019 41St Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc), 2196-2201
Presented at:
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, GERMANY, Jul 23-27, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
1557-170X
ISBN:
978-1-5386-1311-5
Keywords:
Laboratories:


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 Record created 2020-09-12, last modified 2020-10-24

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