University of Oulu

Siirtola, P. (2019). Continuous stress detection using the sensors of commercial smartwatch. Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers - UbiComp/ISWC ’19. Presented at the the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM International Symposium. https://doi.org/10.1145/3341162.3344831

Continuous stress detection using the sensors of commercial smartwatch

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Author: Siirtola, Pekka1
Organizations: 1Biomimetics and Intelligent Systems Group, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019121648252
Language: English
Published: Association for Computing Machinery, 2019
Publish Date: 2019-12-16
Description:

Abstract

Stress detection is becoming a popular field in machine learning and this study focuses on recognizing stress using the sensors of commercially available smartwatches. In most of the previous studies, stress detection is based on partly or fully on electrodermal activity sensor (EDA). However, if the final aim of the study is to build a smartwatch application, using EDA signal is problematic as the smartwatches currently in the market do not include sensor to measure EDA signal. Therefore, this study surveys what sensors the smartwatches currently in the market include, and which of them 3rd party developers have access to. Moreover, it is studied how accurately stress can be detected user-independently using different sensor combinations. In addition, it is studied how detection rates vary between study subjects and what kind of effect window size has to the recognition rates. All of the experiments are based on publicly available WESAD dataset. The results show that, indeed, EDA signal is not necessary when detecting stress user-independently, and therefore, commercial smartwatches can be used for recognizing stress when the used window length is big enough. However, it is also noted that recognition rate varies a lot between the study subjects.

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ISBN Print: 978-1-4503-6869-8
Pages: 1198 - 1201
DOI: 10.1145/3341162.3344831
OADOI: https://oadoi.org/10.1145/3341162.3344831
Host publication: UbiComp/ISWC '19 Adjunct - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Conference: WellComp : International Workshop on Computing for Well-being
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
318 Medical biotechnology
Subjects:
Funding: This research is supported by the Business Finland funding for Reboot IoT Factory-project (www.rebootiotfactory.fi).
Copyright information: © 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp/ISWC '19 Adjunct - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, https://doi.org/10.1145/3341162.3344831.