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

Montesinos, Victoriano ; Dell'Agnola, Fabio ; Arza, Adriana ; Aminifar, Amir LU orcid and Atienza, David (2019) 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS p.2196-2201
Abstract

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. Furthermore, 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... (More)

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. Furthermore, 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 machinelearning and sensor-fusion techniques.

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Please use this url to cite or link to this publication:
author
; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
series title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
article number
8857130
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
conference location
Berlin, Germany
conference dates
2019-07-23 - 2019-07-27
external identifiers
  • scopus:85077869366
  • pmid:31946337
ISSN
1557-170X
ISBN
9781538613115
DOI
10.1109/EMBC.2019.8857130
language
English
LU publication?
no
additional info
Publisher Copyright: © 2019 IEEE.
id
9efec116-cddd-45cc-81ff-dba8f2943b71
date added to LUP
2022-02-05 01:20:39
date last changed
2025-06-21 00:35:42
@inproceedings{9efec116-cddd-45cc-81ff-dba8f2943b71,
  abstract     = {{<p>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. Furthermore, 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 machinelearning and sensor-fusion techniques.</p>}},
  author       = {{Montesinos, Victoriano and Dell'Agnola, Fabio and Arza, Adriana and Aminifar, Amir and Atienza, David}},
  booktitle    = {{2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019}},
  isbn         = {{9781538613115}},
  issn         = {{1557-170X}},
  language     = {{eng}},
  pages        = {{2196--2201}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}},
  title        = {{Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices}},
  url          = {{http://dx.doi.org/10.1109/EMBC.2019.8857130}},
  doi          = {{10.1109/EMBC.2019.8857130}},
  year         = {{2019}},
}