tutorial

Estimating Drivers' Stress from GPS Traces

Authors:

Moushumi Sharmin,

Karen Hovsepian,

Santosh KumarAuthors Info & Claims

AutomotiveUI '14: Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 1 - 8

https://doi.org/10.1145/2667317.2667335

Published: 17 September 2014 Publication History

Abstract

Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days (for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.

References

[1]

Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 6 (1974), 716--723.

[2]

al'Absi, M., and Arnett, D. Adrenocortical responses to psychological stress and risk for hypertension. Biomedecine & Pharmacotherapy 54, 5 (2000), 234--244.

[3]

Bauza, R., et al. Road traffic congestion detection through cooperative vehicle-to-vehicle communications. In IEEE LCN (2010), 606--612.

Digital Library

[4]

Bohannon, R. W. Comfortable and maximum walking speed of adults aged 20--79 years: reference values and determinants. Age and Ageing 26, 1 (1997), 15--19.

[5]

Charette, R. N. Your Car as Stress Monitor?, Accessed: September 2012. http://bit.ly/1oVPLJd.

[6]

Diker, A. C., and Nasibov, E. Estimation of traffic congestion level via fn-dbscan algorithm by using gps data. In IEEE PCI (2012), 1--4.

[7]

Dimsdale, J. Psychological stress and cardiovascular disease. Journal of the American College of Cardiology 51, 13 (2008), 1237--1246.

[8]

Ehlert, P. A., and Rothkrantz, L. J. Microscopic traffic simulation with reactive driving agents. In IEEE ITS (2001), 860--865.

[9]

Ertin, E., et al. Autosense: Unobtrusively wearable sensor suite for inferencing of onset, causality, and consequences of stress in the field. In ACM SenSys (2011), 274--287.

Digital Library

[10]

Ester, M., et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, vol. 96 (1996), 226--231.

Digital Library

[11]

Evans, G. W., and Carrère, S. Traffic congestion, perceived control, and psychophysiological stress among urban bus drivers. Journal of Applied Psychology 76, 5 (1991), 658--663.

[12]

Global Status Report on Road Safety 2013, World Health Organization. http://bit.ly/U9QmvZ.

[13]

Fundamentals of Transportation/Horizontal Curves/Fundamental Horizontal Curve Properties. http://bit.ly/1noRx18.

[14]

Healey, J., and Picard, R. Detecting stress during real-world driving tasks using physiological sensors. IEEE ITS Transactions 6, 2 (2005), 156--166.

Digital Library

[15]

Howard, B. Ford smart car locks your phone when youre stressed or distracted, Accessed: September 2012. http://bit.ly/1rbIl81.

[16]

Kang, H., et al. Decreased expression of synapse-related genes and loss of synapses in major depressive disorder. Nature Medicine 18, 9 (2012), 1413--1417.

[17]

Koslowsky, M., Kluger, A., and Reich, M. Commuting Stress: Causes, Effects and Methods of Coping. Springer, 1995.

[18]

Lee, J. D., et al. Collision warning design to mitigate driver distraction. In ACM SIGCHI conference on Human factors in computing systems (2004), 65--72.

Digital Library

[19]

Likert, R. A technique for the measurement of attitudes. Archives of Psychology (1932).

[20]

Liu, C., and Ye, T. J. Run-off-road crashes: An on-scene perspective, National Highway Traffic Safety Administration. Tech. Rep. DOT HS 811 500, 2011.

[21]

Lu, H., et al. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In ACM UbiComp (2012).

Digital Library

[22]

MacLean, D., et al. Moodwings: a wearable biofeedback device for real-time stress intervention. In Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments (2013), 66.

Digital Library

[23]

Martin Bland, J., and Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. The lancet 327, 8476 (1986), 307--310.

[24]

McEwen, B. Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences 840, 1 (2006), 33--44.

[25]

McEwen, B. Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews 87, 3 (2007), 873--904.

[26]

Nakagawa, S., et al. A general and simple method for obtaining r2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4, 2 (2013), 133--142.

[27]

Nirjon, S., et al. Musicalheart: A hearty way of listening to music. In ACM SenSys (2012), 43--56.

Digital Library

[28]

Novaco, R. W., Stokols, D., and Milanesi, L. Objective and subjective dimensions of travel impedance as determinants of commuting stress. American journal of community psychology 18, 2 (1990), 231--257.

[29]

O'Donovan, A., et al. Stress appraisals and cellular aging: A key role for anticipatory threat in the relationship between psychological stress and telomere length. Brain, Behavior, and Immunity (2012).

[30]

Paschero, M., et al. A real time classifier for emotion and stress recognition in a vehicle driver. In IEEE ISIE (2012), 1690--1695.

[31]

How to find local peaks or valleys in a noisy vector? http://bit.ly/1oArFRk.

[32]

Plarre, K., et al. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In IEEE IPSN (2011), 97--108.

[33]

Quddus, M. A., et al. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15, 5 (2007), 312--328.

[34]

How to find regional minima of an image? http://bit.ly/1oVPxSk.

[35]

Rigas, G., et al. Towards driver's state recognition on real driving conditions. International Journal of Vehicular Technology (2011).

[36]

Rigas, G., et al. Real-time driver's stress event detection. IEEE ITS Transactions 13, 1 (2012), 221--234.

Digital Library

[37]

Sapolsky, R. Why Zebras Don't Get Ulcers. Owl Books, 2004.

[38]

Schneegass, S., et al. A data set of real world driving to assess driver workload. In ACM International Conference on Automotive UI (2013), 150--157.

Digital Library

[39]

Schwarz, G., et al. Estimating the dimension of a model. The Annals of statistics 6, 2 (1978), 461--464.

[40]

Singh, M., and Queyam, A. B. A novel method of stress detection using physiological measurements of automobile drivers. International Journal of Electronics Engineering 5, 2 (2013).

[41]

Singh, R. R., et al. Assessment of driver stress from physiological signals collected under real-time semi-urban driving scenarios. International Journal of Computational Intelligence Systems, ahead-of-print (2013), 1--15.

[42]

Solovey, E. T., et al. Classifying driver workload using physiological and driving performance data: Two field studies. In ACM CHI (2014).

Digital Library

[43]

BMW Steering Wheel monitors your heart, Accessed: September 2012. http://bit.ly/1jJ2BMa.

[44]

Taylor, M. A., et al. Integration of the global positioning system and geographical information systems for traffic congestion studies. Transportation Research Part C: Emerging Technologies 8, 1 (2000), 257--285.

[45]

White, J. B. A Car That Takes Your Pulse, Accessed: September 2012. http://on.wsj.com/1mbbWXj.

[46]

You, C.-W., et al. Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In ACM MobiSys (2013), 13--26.

Digital Library

Cited By

Rony RAhmed MSarcar SAhmed N(2024)Understanding Driving Stress in Urban Bangladesh: An Exploratory Study, Wearable Development and ExperimentACM Journal on Computing and Sustainable Societies10.1145/36484342:2(1-28)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3648434
Hasan MSaha MMahmud MPoudel SWagle SPoudel K(2024)Personalized Stress Detection from Chest-Worn Sensors by Leveraging Machine Learning2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC)10.1109/INTCEC61833.2024.10602865(1-6)Online publication date: 11-Jun-2024
https://doi.org/10.1109/INTCEC61833.2024.10602865
Gomez SVhaduri SWilson MKeller J(2024)Assessing perceived stress, sleep disturbance, and fatigue among pilot and non-pilot traineesSmart Health10.1016/j.smhl.2024.10047232(100472)Online publication date: Jun-2024
https://doi.org/10.1016/j.smhl.2024.100472
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Index Terms

Estimating Drivers' Stress from GPS Traces
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Other conferences

AutomotiveUI '14: Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 2014

287 pages

ISBN:9781450332125

DOI:10.1145/2667317

General Chair:
Linda Ng Boyle
University of Washington, US
,
Program Chairs:
Peter Fröhlich
Telecommunications Research Center Vienna (FTW), Austria
,
Shamsi Iqbal
Microsoft Research, US
,
Gary Burnett
University of Nottingham, UK
,
Publications Chairs:
Erika Miller
University of Washington, US
,
Yuqing Wu
University of Washington, US

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Published: 17 September 2014

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AutomotiveUI '14

AutomotiveUI '14: 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 17 - 19, 2014

WA, Seattle, USA

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AutomotiveUI '14 Paper Acceptance Rate 36 of 79 submissions, 46%;

Overall Acceptance Rate 248 of 566 submissions, 44%

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Cited By

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https://dl.acm.org/doi/10.1145/3648434
Hasan MSaha MMahmud MPoudel SWagle SPoudel K(2024)Personalized Stress Detection from Chest-Worn Sensors by Leveraging Machine Learning2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC)10.1109/INTCEC61833.2024.10602865(1-6)Online publication date: 11-Jun-2024
https://doi.org/10.1109/INTCEC61833.2024.10602865
Gomez SVhaduri SWilson MKeller J(2024)Assessing perceived stress, sleep disturbance, and fatigue among pilot and non-pilot traineesSmart Health10.1016/j.smhl.2024.10047232(100472)Online publication date: Jun-2024
https://doi.org/10.1016/j.smhl.2024.100472
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