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Understanding physiological responses to stressors during physical activity

Published: 05 September 2012 Publication History

Abstract

With advances in physiological sensors, we are able to understand people's physiological status and recognize stress to provide beneficial services. Despite the great potential in physiological stress recognition, there are some critical issues that need to be addressed such as the sensitivity and variability of physiology to many factors other than stress (e.g., physical activity). To resolve these issues, in this paper, we focus on the understanding of physiological responses to both stressor and physical activity and perform stress recognition, particularly in situations having multiple stimuli: physical activity and stressors. We construct stress models that correspond to individual situations, and we validate our stress modeling in the presence of physical activity. Analysis of our experiments provides an understanding on how physiological responses change with different stressors and how physical activity confounds stress recognition with physiological responses. In both objective and subjective settings, the accuracy of stress recognition drops by more than 14% when physical activity is performed. However, by modularizing stress models with respect to physical activity, we can recognize stress with accuracies of 82% (objective stress) and 87% (subjective stress), achieving more than a 5-10% improvement from approaches that do not take physical activity into account.

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Cited By

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  • (2024)Detection and monitoring of stress using wearables: a systematic reviewFrontiers in Computer Science10.3389/fcomp.2024.14788516Online publication date: 18-Dec-2024
  • (2023)Survey on Emotion Sensing Using Mobile DevicesIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322048414:4(2678-2696)Online publication date: 1-Oct-2023
  • (2022)Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercisesCompanion Publication of the 2022 ACM Designing Interactive Systems Conference10.1145/3532107.3532874(12-17)Online publication date: 13-Jun-2022
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    cover image ACM Conferences
    UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing
    September 2012
    1268 pages
    ISBN:9781450312240
    DOI:10.1145/2370216
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 05 September 2012

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    Author Tags

    1. physical activity
    2. physiological responses
    3. stress recognition

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    Ubicomp '12
    Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing
    September 5 - 8, 2012
    Pennsylvania, Pittsburgh

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    UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    Cited By

    View all
    • (2024)Detection and monitoring of stress using wearables: a systematic reviewFrontiers in Computer Science10.3389/fcomp.2024.14788516Online publication date: 18-Dec-2024
    • (2023)Survey on Emotion Sensing Using Mobile DevicesIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322048414:4(2678-2696)Online publication date: 1-Oct-2023
    • (2022)Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercisesCompanion Publication of the 2022 ACM Designing Interactive Systems Conference10.1145/3532107.3532874(12-17)Online publication date: 13-Jun-2022
    • (2021)A Preliminary Experimental Outline to Train Machine Learning Models for the Unobtrusive, Real-Time Detection of Acute Physiological Stress Levels during Training ExercisesProceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference10.1145/3453892.3461833(575-584)Online publication date: 29-Jun-2021
    • (2021)Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous BlinkingProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445577(1-12)Online publication date: 6-May-2021
    • (2021)A Survey of Emotion Recognition using Physiological Signal in Wearable Devices2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)10.1109/AIMS52415.2021.9466092(1-6)Online publication date: 28-Apr-2021
    • (2021)Stressors and Algorithms Used for Stress Detection: a Review2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)10.1109/ACII52823.2021.9597456(1-8)Online publication date: 28-Sep-2021
    • (2020)Mechanism Between Physical Activity and Academic Anxiety: Evidence from PakistanSustainability10.3390/su1209359512:9(3595)Online publication date: 29-Apr-2020
    • (2020)Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With SmartwatchesIEEE Access10.1109/ACCESS.2020.29753518(38146-38163)Online publication date: 2020
    • (2020)User Experience Evaluation: A Validation Study of a Tool-based Approach for Automatic Stress Detection Using Physiological SignalsInternational Journal of Human–Computer Interaction10.1080/10447318.2020.182520537:5(470-483)Online publication date: 4-Oct-2020
    • Show More Cited By

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