A Novel Method of Stress Detection using Physiological Measurements of Automobile Drivers

Stress while driving is an important factor in many number of fatal road accidents worldwide. There has been much work done in driver stress detection. In this research, we present a method based on a correlation analysis and developed a mathematical function for the estimation of automobile driver stress level. The proposed methodology monitors driver’s stress level using features extracted from selected physiological parameters. The results obtained indicate a strong correlation between the stress level of driver and the stress function formed. Threshold approach is used to perform a classification of affective states as “Low Stress”, “Moderate Stress” and “High Stress” based on different traffic conditions. The stress function acts as a direct indicator of stress level of the automobile diver whose physiological parameters are monitored continuously under variable traffic conditions.

[1]  Mandeep Singh,et al.  Correlation between Physiological Parameters of Automobile Drivers and Traffic Conditions , 2013 .

[2]  Lizawati Salahuddin,et al.  Detection of Acute Stress by Heart Rate Variability Using a Prototype Mobile ECG Sensor , 2006 .

[3]  Yang Zhifa,et al.  An active driver fatigue identification technique using multiple physiological features , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[4]  Rahul Banerjee,et al.  Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach , 2009, iiWAS.

[5]  Jennifer A. Healey,et al.  Wearable and automotive systems for affect recognition from physiology , 2000 .

[6]  Matthias Weippert,et al.  Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment , 2007, IEEE Transactions on Fuzzy Systems.

[7]  Jae-Yeon Shin,et al.  Estimation of stress status using biosignal and fuzzy theory , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[8]  J. Cacioppo,et al.  The psychophysiology of emotion. , 1993 .

[9]  Julien Penders,et al.  Trapezius muscle EMG as predictor of mental stress , 2013, TECS.

[10]  Ahmet Akbas.  Evaluation of the physiological data indicating the dynamic stress level of drivers , 2011 .

[11]  Gonzalo Bailador,et al.  Stress detection by means of stress physiological template , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[12]  Guanghua Wu,et al.  The Analysis of Emotion Recognition from GSR Based on PSO , 2010, 2010 International Symposium on Intelligence Information Processing and Trusted Computing.

[13]  John L. Sibert,et al.  Heart rate variability: indicator of user state as an aid to human-computer interaction , 1998, CHI.

[14]  Dong Hee Lee,et al.  Automobile driver's stress index provision system that utilizes electrocardiogram , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[15]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[16]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[17]  Rudy E. Ballieux,et al.  Cardiovascular and endocrine responses to experimental stress: Effects of mental effort and controllability , 1998, Psychoneuroendocrinology.

[18]  Fang Chen,et al.  Galvanic skin response (GSR) as an index of cognitive load , 2007, CHI Extended Abstracts.

[19]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[20]  D.I. Fotiadis,et al.  A reasoning-based framework for car driver’s stress prediction , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[21]  P. Hassmén,et al.  Psychophysiological stress and emg activity of the trapezius muscle , 1994, International journal of behavioral medicine.

[22]  A. Barreto,et al.  Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  I. Homma,et al.  Breathing rhythms and emotions , 2008, Experimental physiology.

[24]  Georgios N. Yannakakis,et al.  Correlation between heart rate, electrodermal activity and player experience in first-person shooter games , 2010, Sandbox '10.

[25]  C. Tsigos,et al.  Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. , 2002, Journal of psychosomatic research.

[26]  Abdullah Bin Queyam,et al.  Stress Detection in Automobile Drivers using Physiological Parameters: A Review , 2013 .

[27]  N. Carlson.  Physiology of behavior , 1977 .

[28]  R. R. Singh,et al.  Biosignal based on-road stress monitoring for automotive drivers , 2012, 2012 National Conference on Communications (NCC).

[29]  Yong Deng,et al.  Evaluating feature selection for stress identification , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[30]  Mohammad Ghavami,et al.  A Novel Blind Watermarking of ECG Signals on Medical Images Using EZW Algorithm , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.