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.
- Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 6 (1974), 716--723.Google ScholarCross Ref
- al'Absi, M., and Arnett, D. Adrenocortical responses to psychological stress and risk for hypertension. Biomedecine & Pharmacotherapy 54, 5 (2000), 234--244.Google Scholar
- Bauza, R., et al. Road traffic congestion detection through cooperative vehicle-to-vehicle communications. In IEEE LCN (2010), 606--612. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Charette, R. N. Your Car as Stress Monitor?, Accessed: September 2012. http://bit.ly/1oVPLJd.Google Scholar
- 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.Google Scholar
- Dimsdale, J. Psychological stress and cardiovascular disease. Journal of the American College of Cardiology 51, 13 (2008), 1237--1246.Google ScholarCross Ref
- Ehlert, P. A., and Rothkrantz, L. J. Microscopic traffic simulation with reactive driving agents. In IEEE ITS (2001), 860--865.Google Scholar
- 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. Google ScholarDigital Library
- Ester, M., et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, vol. 96 (1996), 226--231.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Global Status Report on Road Safety 2013, World Health Organization. http://bit.ly/U9QmvZ.Google Scholar
- Fundamentals of Transportation/Horizontal Curves/Fundamental Horizontal Curve Properties. http://bit.ly/1noRx18.Google Scholar
- Healey, J., and Picard, R. Detecting stress during real-world driving tasks using physiological sensors. IEEE ITS Transactions 6, 2 (2005), 156--166. Google ScholarDigital Library
- Howard, B. Ford smart car locks your phone when youre stressed or distracted, Accessed: September 2012. http://bit.ly/1rbIl81.Google Scholar
- 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.Google ScholarCross Ref
- Koslowsky, M., Kluger, A., and Reich, M. Commuting Stress: Causes, Effects and Methods of Coping. Springer, 1995.Google Scholar
- 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. Google ScholarDigital Library
- Likert, R. A technique for the measurement of attitudes. Archives of Psychology (1932).Google Scholar
- 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.Google Scholar
- Lu, H., et al. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In ACM UbiComp (2012). Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Martin Bland, J., and Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. The lancet 327, 8476 (1986), 307--310.Google Scholar
- McEwen, B. Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences 840, 1 (2006), 33--44.Google Scholar
- McEwen, B. Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews 87, 3 (2007), 873--904.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Nirjon, S., et al. Musicalheart: A hearty way of listening to music. In ACM SenSys (2012), 43--56. Google ScholarDigital Library
- 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.Google Scholar
- 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).Google Scholar
- Paschero, M., et al. A real time classifier for emotion and stress recognition in a vehicle driver. In IEEE ISIE (2012), 1690--1695.Google Scholar
- How to find local peaks or valleys in a noisy vector? http://bit.ly/1oArFRk.Google Scholar
- Plarre, K., et al. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In IEEE IPSN (2011), 97--108.Google Scholar
- 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.Google ScholarCross Ref
- How to find regional minima of an image? http://bit.ly/1oVPxSk.Google Scholar
- Rigas, G., et al. Towards driver's state recognition on real driving conditions. International Journal of Vehicular Technology (2011).Google Scholar
- Rigas, G., et al. Real-time driver's stress event detection. IEEE ITS Transactions 13, 1 (2012), 221--234. Google ScholarDigital Library
- Sapolsky, R. Why Zebras Don't Get Ulcers. Owl Books, 2004.Google Scholar
- 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. Google ScholarDigital Library
- Schwarz, G., et al. Estimating the dimension of a model. The Annals of statistics 6, 2 (1978), 461--464.Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- Solovey, E. T., et al. Classifying driver workload using physiological and driving performance data: Two field studies. In ACM CHI (2014). Google ScholarDigital Library
- BMW Steering Wheel monitors your heart, Accessed: September 2012. http://bit.ly/1jJ2BMa.Google Scholar
- 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.Google ScholarCross Ref
- White, J. B. A Car That Takes Your Pulse, Accessed: September 2012. http://on.wsj.com/1mbbWXj.Google Scholar
- You, C.-W., et al. Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In ACM MobiSys (2013), 13--26. Google ScholarDigital Library
Index Terms
- Estimating Drivers' Stress from GPS Traces
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