research-article

Continuous Detection of Physiological Stress with Commodity Hardware

Authors:

Stephanie Lewia,

David KotzAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 1, Issue 2

Article No.: 8, Pages 1 - 30

https://doi.org/10.1145/3361562

Published: 11 April 2020 Publication History

Abstract

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.

References

[1]

M. Al’Absi and D. K. Arnett. 2000. Adrenocortical responses to psychological stress and risk for hypertension. Biomedicine 8 Pharmacotherapy 54, 5 (2000), 234--244.

[2]

Mustafa Al’Absi, Stephan Bongard, Tony Buchanan, Gwendolyn A. Pincomb, Julio Licinio, and William R. Lovallo. 1997. Cardiovascular and neuroendocrine adjustment to public speaking and mental arithmetic stressors. Psychophysiology 34, 3 (May 1997), 266--275.

[3]

Marco Altini. 2015. Hardware for HRV: What Sensor Should You Use? Retrieved February 18, 2020 from https://www.hrv4training.com/blog/hardware-for-hrv-what-sensor-should-you-use.

[4]

Android Central. 2016. Heart Rate Monitor Horribly Inaccurate? Retrieved February 18, 2020 from https://forums.androidcentral.com/samsung-gear-2-gear-2-neo/380007-heart-rate-monitor-horribly-inaccurate.html.

[5]

Andrew Baum. 1990. Stress, intrusive imagery, and chronic distress. Health Psychology 9, 6 (1990), 653.

[6]

George E. Billman. 2013. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Frontiers in Physiology 4 (2013), 26.

[7]

Biopac Systems Inc.2016. Biopac MP150. Retrieved February 18, 2020 from https://www.biopac.com/wp-content/uploads/MP150-Systems.pdf.

[8]

George Boateng, Ryan Halter, John A. Batsis, and David Kotz. 2017. ActivityAware: An app for real-time daily activity level monitoring on the amulet wrist-worn device. In Proceedings of the IEEE PerCom Workshop on Pervasive Health Technologies (PerHealth’17). IEEE, Los Alamitos, CA, 431--435.

[9]

George Boateng, Vivian Genaro Motti, Varun Mishra, John A. Batsis, Josiah Hester, and David Kotz. 2019. Experience: Design, development and evaluation of a wearable device for mHealth applications. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom’19).

[10]

Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, and Alex Sandy Pentland. 2014. Pervasive stress recognition for sustainable living. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, (PerCom Workshops’14). IEEE, Los Alamitos, CA, 345--350.

[11]

J. J. Braithwaite, D. G. Watson, R. Jones, and M. Rowe. 2013. A guide for analysing electrodermal activity (EDA) 8; skin conductance responses (SCRs) for psychological experiments. Psychophysiology 49, 8 (2013), 1--42.

[12]

Grace Chen, Varun Mishra, and Ching-Hua Chen. 2019. Temporal factors of listening to music on stress reduction. In 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 (UbiComp/ISWC’19 Adjunct). ACM, New York, NY, 907--914.

[13]

Jongyoon Choi, B. Ahmed, and Ricardo Gutierrez-Osuna. 2012. Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Transactions on Information Technology in Biomedicine 16, 2 (2012), 279--286.

Digital Library

[14]

George P. Chrousos and Philip W. Gold. 1992. The concepts of stress and stress system disorders: Overview of physical and behavioral homeostasis. JAMA 267, 9 (1992), 1244--1252.

[15]

Matteo Ciman, Katarzyna Wac, and Ombretta Gaggi. 2015. iSenseStress: Assessing stress through human-smartphone interaction analysis. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare (PerHealth’15). 84--91.

[16]

Sheldon Cohen, Tom Kamarck, and Robin Mermelstein. 1983. A global measure of perceived stress. Journal of Health and Social Behavior 24, 4 (1983), 385--396.

[17]

Brian Edwards. 2011. Wearable Sensor by Affectiva Can Measure Anxiety and Is Helping Autism Research. Retrieved February 18, 2020 from https://www.imedicalapps.com/2011/10/wearable-sensor-by-affectiva-can-measure-anxiety-and-is-helping-autism-research/.

[18]

Begum Egilmez, Emirhan Poyraz, Wenting Zhou, Gokhan Memik, Peter Dinda, and Nabil Alshurafa. 2017. UStress: Understanding college student subjective stress using wrist-based passive sensing. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops’17). IEEE, Los Alamitos, CA, 673--678.

[19]

Emaptica. 2018. Empatica E4. Retrieved February 18, 2020 from https://www.empatica.com/en-eu/research/e4/.

[20]

Michael R. Esco and Andrew A. Flatt. 2014. Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. Journal of Sports Science and Medicine 13, 3 (Sept. 2014), 535--541. http://www.ncbi.nlm.nih.gov/ /25177179.

[21]

S. M. Fox and W. L. Haskell. 1970. The exercise stress test: Needs for standardization. In Cardiology: Current Topics and Progress. Academic Press, New York, NY, 149--154.

[22]

Ulla Fredriksson-Larsson, Eva Brink, Gunne Grankvist, Ingibjörg H. Jonsdottir, and Pia Alsen. 2015. The single-item measure of stress symptoms after myocardial infarction and its association with fatigue. Open Journal of Nursing 5, 04 (2015), 345.

[23]

Maurizio Garbarino, Matteo Lai, Simone Tognetti, Rosalind Picard, and Daniel Bender. 2014. Empatica E3—A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In Proceedings of the International Conference on Wireless Mobile Communication and Healthcare.

[24]

Enrique Garcia-Ceja, Venet Osmani, and Oscar Mayora. 2016. Automatic stress detection in working environments from smartphones’ accelerometer data: A first step. IEEE Journal of Biomedical and Health Informatics 20, 4 (July 2016), 1053--1060.

[25]

Martin Gjoreski, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. 2016. Continuous stress detection using a wrist device: In laboratory and real life. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp’16 Adjunct). ACM, New York, NY, 1185--1193.

[26]

Martin Gjoreski, Mitja Luštrek, Matjaž Gams, and Hristijan Gjoreski. 2017. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics 73 (Sept. 2017), 159--170.

Digital Library

[27]

Kristina Grifantini. 2010. Sensor Detects Emotions Through the Skin. Retrieved February 18, 2020 from https://www.technologyreview.com/s/421316/sensor-detects-emotions-through-the-skin/.

[28]

Tian Hao, Kimberly N. Walter, Marion J. Ball, Hung-Yang Chang, Si Sun, and Xinxin Zhu. 2017. StressHacker: Towards practical stress monitoring in the wild with smartwatches. In Proceedings of the AMIA Annual Symposium.

[29]

Jennifer A. Healey and Rosalind W. Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6, 2 (2005), 156--166.

Digital Library

[30]

Javier Hernandez, Rob R. Morris, and Rosalind W. Picard. 2011. Call center stress recognition with person-specific models. In Affective Computing and Intelligent Interaction. Lecture Notes in Computer Science, Vol. 6974. Springer, 125--134 pages.

[31]

Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, Kevin Storer, et al. 2016. Amulet: An energy-efficient, multi-application wearable platform. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’16). ACM, New York, NY, 216--229.

[32]

Karen Hovsepian, Mustafa Al’Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: Towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 493--504.

[33]

Martin Kusserow, Oliver Amft, and Gerhard Troster. 2013. Monitoring stress arousal in the wild. IEEE Pervasive Computing 12, 2 (April 2013), 28--37.

Digital Library

[34]

Sylvain Laborde, Emma Mosley, and Julian F. Thayer. 2017. Heart rate variability and cardiac vagal tone in psychophysiological research—Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology 8 (2017), 213.

[35]

Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, Marianne S. Mast, Gokul T. Chittaranjan, Andrew T. Campbell, Daniel G. Perez, and Tanzeem Choudhury. 2012. StressSense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp’12).

Digital Library

[36]

Bruce S. McEwen and Eliot Stellar. 1993. Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine 153, 18 (1993), 2093--2101.

[37]

Varun Mishra, Tian Hao, Si Sun, Kimberly N. Walter, Marion J. Ball, Ching-Hua Chen, and Xinxin Zhu. 2018. Investigating the role of context in perceived stress detection in the wild. In Proceedings of the 2018 ACM International Joint Conference and the 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp’18). ACM, New York, NY, 1708--1716.

[38]

Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. 2018. The case for a commodity hardware solution for stress detection. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’18). ACM, New York, NY, 1717--1728.

[39]

Amir Muaremi, Bert Arnrich, and Gerhard Tröster. 2013. Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3, 2 (2013), 172--183.

[40]

Amir Muaremi, Agon Bexheti, Franz Gravenhorst, Bert Arnrich, and Gerhard Troster. 2014. Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’14). IEEE, Los Alamitos, CA, 185--188.

[41]

NIMH. 2016. 5 Things You Should Know About Stress. Retrieved February 18, 2020 from https://www.nimh.nih.gov/health/publications/stress/index.shtml.

[42]

K. Plarre, A. Raij, S. M. Hossain, A. A. Ali, M. Nakajima, M. Al’Absi, E. Ertin, et al. 2011. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In Proceedings of the IEEE International Conference on Information Processing in Sensor Networks (IPSN’11). IEEE, Los Alamitos, CA, 97--108. http://ieeexplore.ieee.org/xpls/abs_all.jsp? arnumber=5779068.

[43]

Polar. 2017. Polar H7. Retrieved February 18, 2020 from https://support.polar.com/us-en/support/H7_heart_rate_sensor

[44]

Gunnar C. Pope, Varun Mishra, Stephanie Lewia, Byron Lowens, David Kotz, Sarah Lord, and Ryan Halter. 2018. An ultra-low resource wearable EDA sensor using wavelet compression. In Proceedings of the IEEE Conference on Body Sensor Networks (BSN’18). 193--196.

[45]

R. Rosmond and P. Björntorp. 1998. Endocrine and metabolic aberrations in men with abdominal obesity in relation to anxio-depressive infirmity. Metabolism 47, 10 (1998), 1187--1193.

[46]

Pedro Sanches, Kristina Höök, Elsa Vaara, Claus Weymann, Markus Bylund, Pedro Ferreira, Nathalie Peira, and Marie Sjölinder. 2010. Mind the body! Designing a mobile stress management application encouraging personal reflection. In Proceedings of the ACM Conference on Designing Interactive Systems. ACM, New York, NY, 47--56.

[47]

Akane Sano and Rosalind W. Picard. 2013. Stress recognition using wearable sensors and mobile phones. In Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, Los Alamitos, CA, 671--676.

[48]

Hillol Sarker, Inbal Nahum-Shani, Mustafa Al’Absi, Santosh Kumar, Matthew Tyburski, Md M. Rahman, Karen Hovsepian, et al. 2016. Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, NY, 4489--4501.

[49]

Fred Shaffer and J. P. Ginsberg. 2017. An overview of heart rate variability metrics and norms. Frontiers in Public Health 5 (2017), 258.

[50]

Feng-Tso Sun, Cynthia Kuo, Heng-Tze Cheng, Senaka Buthpitiya, Patricia Collins, and Martin Griss. 2012. Activity-aware mental stress detection using physiological sensors. In Mobile Computing, Applications, and Services. Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering, Vol. 76. Springer, 211--230.

[51]

H. Tanaka, K. D. Monahan, and D. R. Seals. 2001. Age-predicted maximal heart rate revisited.Journal of the American College of Cardiology 37, 1 (Jan. 2001), 153--156. http://www.ncbi.nlm.nih.gov/ /11153730.

[52]

Sara Taylor, Natasha Jaques, Weixuan Chen, Szymon Fedor, Akane Sano, and Rosalind Picard. 2015. Automatic identification of artifacts in electrodermal activity data. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Vol. 2015. 1934--1937.

[53]

Julian F. Thayer, Fredrik Åhs, Mats Fredrikson, John J. Sollers, and Tor D. Wager. 2012. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews 36, 2 (Feb. 2012, 747--756.

[54]

Vincent W. S. Tseng, Saeed Abdullah, Min Hane Aung, Franziska Wittleder, Michael Merrill, and Tanzeem Choudhury. 2016. Assessing mental health issues on college campuses: Preliminary findings from a pilot study. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp’16 Adjunct). ACM, New York, NY, 1200--1208.

Digital Library

[55]

Rui Wang, Peilin Hao, Xia Zhou, Andrew T. Campbell, and Gabriella Harari. 2015. SmartGPA: How smartphones can assess and predict academic performance of college students. GetMobile: Mobile Computing and Communications 19, 4 (Oct. 2015), 13--17.

[56]

M. Wu. 2006. Trimmed and Winsorized Estimators. Ph.D. Dissertation. Michigan State University.

[57]

Zephyr. 2018. Zephyr BioHarness 3. Retrieved February 18, 2020 from https://www.zephyranywhere.com/media/download/bioharness3-user-manual.pdf.

[58]

Amazon. 2020. BioHarness 3 - Wireless Professional Heart Rate 8 Physiological Monitor with Bluetooth. https://www.amazon.com/BioHarness-Wireless-Professional-Physiological-Bluetooth/dp/B009ZUYNCW.

Cited By

Lazarou EExarchos T(2024)Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devicesAIMS Neuroscience10.3934/Neuroscience.202400611:2(76-102)Online publication date: 2024
https://doi.org/10.3934/Neuroscience.2024006
Toshnazarov KLee UKim BMishra VNajarro LNoh Y(2024) SOSW : Stress Sensing With Off-the-Shelf Smartwatches in the Wild IEEE Internet of Things Journal10.1109/JIOT.2024.337529911:12(21527-21545)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JIOT.2024.3375299
Pinge AJaisinghani DGhosh SChalla ASen S(2024)mTanaaw: A System for Assessment and Analysis of Mental Health with Wearables2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427432(105-110)Online publication date: 3-Jan-2024
https://doi.org/10.1109/COMSNETS59351.2024.10427432
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Index Terms

Continuous Detection of Physiological Stress with Commodity Hardware
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 1, Issue 2

April 2020

90 pages

EISSN:2637-8051

DOI:10.1145/3387924

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Publication History

Published: 11 April 2020

Accepted: 01 September 2019

Revised: 01 July 2019

Received: 01 November 2018

Published in HEALTH Volume 1, Issue 2

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Cited By

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https://doi.org/10.3934/Neuroscience.2024006
Toshnazarov KLee UKim BMishra VNajarro LNoh Y(2024) SOSW : Stress Sensing With Off-the-Shelf Smartwatches in the Wild IEEE Internet of Things Journal10.1109/JIOT.2024.337529911:12(21527-21545)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JIOT.2024.3375299
Pinge AJaisinghani DGhosh SChalla ASen S(2024)mTanaaw: A System for Assessment and Analysis of Mental Health with Wearables2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427432(105-110)Online publication date: 3-Jan-2024
https://doi.org/10.1109/COMSNETS59351.2024.10427432
McAlister KZink JHuh JYang CDunton GDieli-Conwright CPage KBelcher B(2024)Perceived stress and associations between physical activity, sedentary time, and interstitial glucose in healthy adolescentsPhysiology & Behavior10.1016/j.physbeh.2024.114617283(114617)Online publication date: Sep-2024
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Yamane NMishra VGoodwin M(2024)HeartView: An Extensible, Open-Source, Web-Based Signal Quality Assessment Pipeline for Ambulatory Cardiovascular DataPervasive Computing Technologies for Healthcare10.1007/978-3-031-59717-6_8(107-123)Online publication date: 4-Jun-2024
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Siirtola PTamminen SChandra GIhalapathirana ARöning J(2023)Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable SensorsSensors10.3390/s2303159823:3(1598)Online publication date: 1-Feb-2023
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Doucet MBrisebois HMcKerral M(2023)Heart Rate Variability in Concussed College Athletes: Follow-Up Study and Biological Sex DifferencesBrain Sciences10.3390/brainsci1312166913:12(1669)Online publication date: 1-Dec-2023
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Shahbazi ZByun Y(2023)Early Life Stress Detection Using Physiological Signals and Machine Learning PipelinesBiology10.3390/biology1201009112:1(91)Online publication date: 6-Jan-2023
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Tervonen JNärväinen JMäntyjärvi JPettersson K(2023)Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress TestFrontiers in Neuroergonomics10.3389/fnrgo.2023.12942864Online publication date: 5-Dec-2023
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Toshnazarov KLee TNoh Y(2023)WildStress: exploring rich situational contexts for stress detection in the wild2023 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp57234.2023.00091(381-382)Online publication date: Mar-2023
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