Multimodal data collection framework for mental stress monitoring

Saewon Kye; Junhyung Moon; Juneil Lee; Inho Choi; Dongmi Cheon; Kyoungwoo Lee

doi:10.1145/3123024.3125616

Multimodal data collection framework for mental stress monitoring

Saewon Kye, Junhyung Moon, Juneil Lee, Inho Choi, Dongmi Cheon, Kyoungwoo Lee

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Accurate recognition of people's responses to stress and timely management of stress is one of the important aspects of maintaining good health. However, recognizing such reactions is difficult since people react to stressful events in various ways. Accordingly, we propose a multi-modal stress monitoring framework to examine people's physiological and behavioral reactions to stressors. Our framework consists of (i) multimodal data collection related to mental stress, (ii) stress inducing experiment in a laboratory, and (iii) signal examination software in real-time and after the data collection. Through preliminary experiments, we have examined how people show various reactions to different kinds of stressful tasks. Finally, based on the proposed framework, we will establish a database contributing to the mental health sensing research.

Original language	English
Title of host publication	UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
Publisher	Association for Computing Machinery, Inc
Pages	822-829
Number of pages	8
ISBN (Electronic)	9781450351904
DOIs	https://doi.org/10.1145/3123024.3125616
Publication status	Published - 2017 Sept 11
Event	2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 - Maui, United States Duration: 2017 Sept 11 → 2017 Sept 15

Publication series

Name	UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Other

Other	2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017
Country/Territory	United States
City	Maui
Period	17/9/11 → 17/9/15

Bibliographical note

Publisher Copyright:
Copyright © 2017 ACM.

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3123024.3125616

Cite this

Kye, S., Moon, J., Lee, J., Choi, I., Cheon, D., & Lee, K. (2017). Multimodal data collection framework for mental stress monitoring. In UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 822-829). (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123024.3125616

Kye, Saewon ; Moon, Junhyung ; Lee, Juneil et al. / Multimodal data collection framework for mental stress monitoring. UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc, 2017. pp. 822-829 (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers).

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abstract = "Accurate recognition of people's responses to stress and timely management of stress is one of the important aspects of maintaining good health. However, recognizing such reactions is difficult since people react to stressful events in various ways. Accordingly, we propose a multi-modal stress monitoring framework to examine people's physiological and behavioral reactions to stressors. Our framework consists of (i) multimodal data collection related to mental stress, (ii) stress inducing experiment in a laboratory, and (iii) signal examination software in real-time and after the data collection. Through preliminary experiments, we have examined how people show various reactions to different kinds of stressful tasks. Finally, based on the proposed framework, we will establish a database contributing to the mental health sensing research.",

author = "Saewon Kye and Junhyung Moon and Juneil Lee and Inho Choi and Dongmi Cheon and Kyoungwoo Lee",

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Kye, S, Moon, J, Lee, J, Choi, I, Cheon, D & Lee, K 2017, Multimodal data collection framework for mental stress monitoring. in UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Association for Computing Machinery, Inc, pp. 822-829, 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017, Maui, United States, 17/9/11. https://doi.org/10.1145/3123024.3125616

Multimodal data collection framework for mental stress monitoring. / Kye, Saewon; Moon, Junhyung; Lee, Juneil et al.

UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc, 2017. p. 822-829 (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Multimodal data collection framework for mental stress monitoring

AU - Kye, Saewon

AU - Moon, Junhyung

AU - Lee, Juneil

AU - Choi, Inho

AU - Cheon, Dongmi

AU - Lee, Kyoungwoo

PY - 2017/9/11

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AB - Accurate recognition of people's responses to stress and timely management of stress is one of the important aspects of maintaining good health. However, recognizing such reactions is difficult since people react to stressful events in various ways. Accordingly, we propose a multi-modal stress monitoring framework to examine people's physiological and behavioral reactions to stressors. Our framework consists of (i) multimodal data collection related to mental stress, (ii) stress inducing experiment in a laboratory, and (iii) signal examination software in real-time and after the data collection. Through preliminary experiments, we have examined how people show various reactions to different kinds of stressful tasks. Finally, based on the proposed framework, we will establish a database contributing to the mental health sensing research.

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M3 - Conference contribution

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ER -

Kye S, Moon J, Lee J, Choi I, Cheon D, Lee K. Multimodal data collection framework for mental stress monitoring. In UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc. 2017. p. 822-829. (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers). doi: 10.1145/3123024.3125616

Multimodal data collection framework for mental stress monitoring

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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Cite this