Abstract
There are a lot of applications were developed to take advance of smartphone sensors for utilizing the personal services such as health-care, walk-counting, routing etc. Users behavior analysis is attracted a lot of researches interested with various approaches. We proposed a novel framework to detect the abnormal driving behavior using smartphone sensors. It named Abnormal Behavior Detection System (ABDS). The system keep track the driver activities during he’s trip based on smartphone sensors. The Practice Swarm Optimization (PSO) algorithm is used to automatically select suitable features extracted from sensors data. The oriented accelerometer is used to detect activity. The abnormal behavior is collected and labeled then detection by Artificial Neural Network (ANN). The implementation shown the promising results in case of seven activities (stop, moving, acceleration, deceleration, turn left, turn right and U-turn) with 86.71% accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Reyes-Ortiz, J.-L., Oneto, L., Sama, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)
Gomes, J.B.R., Krishnaswamy, S., Gaber, M.M., Sousa, P.A., Menasalvas, E.: MARS: a personalised mobile activity recognition system. In: 2012 IEEE 13th International Conference on Mobile Data Management (MDM), pp. 316–319. IEEE, July 2012. https://doi.org/10.1109/MDM.2012.33
Kusuma, A., Liu, R., Montgomery, F.: Gap acceptance behavior in motorway weaving sections. In: Proceedings of the Eastern Asia Society for Transportation Studies, vol. 9 (2013)
Li, F., Zhang, H., Che, H., Qiu, X.: Dangerous driving behavior detection using smartphone sensors, pp. 1902–1907 (2016)
Xu, H., Zhang, L., Zhai, W.: Detection of human movement behavior rules using three-axis acceleration sensor. In: Jin, D., Lin, S. (eds.) Advances in Multimedia, Software Engineering and Computing Vol. 1. Advances in Intelligent and Soft Computing, vol. 128, pp. 647–652. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25989-0_103
Jain, A., Kanhangad, V.: Exploring orientation and accelerometer sensor data for personal authentication in smartphones using touchscreen gestures. Pattern Recogn. Lett. 68, 351–360 (2015)
Kalra, N., Bansal, D.: Analyzing driver behavior using smartphone sensors: a survey. Int. J. Electron. Electr. Eng. 7(7), 697–702 (2014)
Ferrer, S., Ruiz, T.: Travel behavior characterization using raw accelerometer data collected from smartphones. Procedia - Soc. Behav. Sci. 160, 140–149 (2014)
Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 34, 450–457 (2014)
Yu, J., Chen, Z., Zhu, Y., Chen, Y., Kong, L., Li, M.: Fine-grained abnormal driving behaviors detection and identification with smartphones. IEEE Trans. Mob. Comput. 16(8), 2198–2212 (2017)
Vavouranakis, P., Panagiotakis, S., Mastorakis, G., Mavromoustakis, C.X., Batalla, J.M.: Recognizing driving behaviour using smartphones. In: Batalla, J.M., Mastorakis, G., Mavromoustakis, C.X., Pallis, E. (eds.) Beyond the Internet of Things. IT, pp. 269–299. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50758-3_11
Lu, D.-N., Nguyen, T.-T., Ngo, T.-T., Nguyen, T.-H., Nguyen, H.-N.: Mobile online activity recognition system based on smartphone sensors. In: Akagi, M., Nguyen, T.-T., Vu, D.-T., Phung, T.-N., Huynh, V.-N. (eds.) ICTA 2016. AISC, vol. 538, pp. 357–366. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49073-1_39
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43 (1995)
Liu, Z., Wu, M., Zhu, K., Zhang, L.: SenSafe: a smartphone-based traffic safety framework by sensing vehicle and pedestrian behaviors. Mob. Inf. Syst. 2016, 1–13 (2016)
Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014)
Acknowledgments
This work has been supported by Vietnam National University, Hanoi (VNU) under project No. QG.17.39.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Lu, DN., Tran, TB., Nguyen, DN., Nguyen, TH., Nguyen, HN. (2018). Abnormal Behavior Detection Based on Smartphone Sensors. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC ICCASA 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-77818-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-77818-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77817-4
Online ISBN: 978-3-319-77818-1
eBook Packages: Computer ScienceComputer Science (R0)