Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : G.P.Dineshkumar, J.JafarAli, M.Dinesh, N.Dharungeeran

Volume 01 Issue o4  April 2014 

ISSN no:  2348-3121 

Page no: 1-4

Abstract  In this work, this paper address a fundamental and critical task of detecting the behavior of driving and texting using smartphones carried by users. This paper propose, design, and implement TEXIVE that leverages various sensors integrated in the smartphone and realizes our goal of distinguishing drivers and passengers and detecting texting using rich user micro movements and irregularities that can be detected by sensors in the phone before and during driving and texting. Without relying on external infrastructure, TEXIVE has an advantage of being readily implemented and adopted, while at the same time raising a number of challenges that need to be carefully addressed for achieving a successful detection with good sensitivity, specificity, accuracy, and precision. Our system distinguishes the driver and passengers by detecting whether a user is entering a vehicle or not, inferring which side of the vehicle is entering, reasoning whether the user is sitting in front or rear seats, and discovering if a user is texting by fusing multiple evidences collected from accelerometer, magnetometer, and gyroscope sensors. To validate our approach, this paper conduct extensive experiments with several users on various vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

Keywords accelerometer,smartphone,gyroscope sensors


[1] Distraction. http://www.distraction.gov/.
[2] Insurance institute for highway safety. http://www.iihs.gov/.
[3] National highway traffic safety administration (nhtsa). http://www.nhtsa.gov/.
[4] Rode dog. http://http://www.rodedog.com/.
[5] AHMED, N., NATARAJAN, T., AND RAO, K. Discrete cosine transform. Computers, IEEE Transactions on 100, 1 (1974), 90–93.
[6] BAO, L., AND INTILLE, S. Activity recognition from user-annotated. acceleration data. Pervasive Computing (2004), 1-17.
[7] BERGASA, L., NUEVO, J., SOTELO, M., BAREA, R., AND LOPEZ, M. Real-time system for monitoring driver vigilance. Intelligent Transportation Systems, IEEE Transactions on 7, 1 (2006), 63–77.

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