research-article

Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors

Authors:

Md. Mahbubur Rahman,

Nazir Saleheen,

Megan Battles Parsons,

Eugene H. Buder,

Santosh KumarAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 4

Article No.: 117, Pages 1 - 23

https://doi.org/10.1145/3432210

Published: 18 December 2020 Publication History

Abstract

Stressful conversation is a frequently occurring stressor in our daily life. Stressors not only adversely affect our physical and mental health but also our relationships with family, friends, and coworkers. In this paper, we present a model to automatically detect stressful conversations using wearable physiological and inertial sensors. We conducted a lab and a field study with cohabiting couples to collect ecologically valid sensor data with temporally-precise labels of stressors. We introduce the concept of stress cycles, i.e., the physiological arousal and recovery, within a stress event. We identify several novel features from stress cycles and show that they exhibit distinguishing patterns during stressful conversations when compared to physiological response due to other stressors. We observe that hand gestures also show a distinct pattern when stress occurs due to stressful conversations. We train and test our model using field data collected from 38 participants. Our model can determine whether a detected stress event is due to a stressful conversation with an F1-score of 0.83, using features obtained from only one stress cycle, facilitating intervention delivery within 3.9 minutes since the start of a stressful conversation.

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Cited By

Sahu NGupta SLone H(2024)Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious ActivitiesACM Journal on Computing and Sustainable Societies10.1145/36636712:2(1-23)Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1145/3663671
Neupane SSaha MAli NHnat TSamiei SNandugudi AAlmeida DKumar S(2024)Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with WearablesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642662(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642662
Morshed MMahbubur Rahman MNathan VZhu LBae JRosa CMendes WKuang JGao A(2024)Core Body Temperature and its Role in Detecting Acute Stress: A Feasibility StudyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447599(1606-1610)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447599
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Index Terms

Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 4

December 2020

1356 pages

EISSN:2474-9567

DOI:10.1145/3444864

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Copyright © 2020 ACM.

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Published: 18 December 2020

Published in IMWUT Volume 4, Issue 4

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Cited By

Sahu NGupta SLone H(2024)Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious ActivitiesACM Journal on Computing and Sustainable Societies10.1145/36636712:2(1-23)Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1145/3663671
Neupane SSaha MAli NHnat TSamiei SNandugudi AAlmeida DKumar S(2024)Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with WearablesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642662(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642662
Morshed MMahbubur Rahman MNathan VZhu LBae JRosa CMendes WKuang JGao A(2024)Core Body Temperature and its Role in Detecting Acute Stress: A Feasibility StudyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447599(1606-1610)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447599
King ZYu HVaessen TMyin-Germeys ISano A(2023)Towards the Understanding of Receptivity and Affect in EMAs using Physiological based Machine Learning Method: Analysis of Receptivity and Affect (Preprint)JMIR mHealth and uHealth10.2196/46347Online publication date: 8-Feb-2023
https://doi.org/10.2196/46347
Liang DZhang AThomaz E(2023)Automated Face-To-Face Conversation Detection on a Commodity Smartwatch with Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108827:3(1-29)Online publication date: 27-Sep-2023
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Meier MHolz C(2023)BMARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962687:2(1-21)Online publication date: 12-Jun-2023
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Ekambaranathan AZhao JChalhoub G(2023)Navigating the Data AvalancheProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962677:2(1-24)Online publication date: 12-Jun-2023
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Chen MLu LWang JYu JChen YWang ZBa ZLin FRen K(2023)VoiceCloakProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962667:2(1-21)Online publication date: 12-Jun-2023
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Lambrichts MRamakers RHodges SDevine JUnderwood LFinney J(2023)CircuitGIueProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962657:2(1-30)Online publication date: 12-Jun-2023
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