iBump: Smartphone application to detect car accidents

Traffic accidents are a fact of life. While accidents are sometimes unavoidable, studies show that the long response time required for emergency responders to arrive is a primary reason behind increased fatalities in serious accidents. One way to reduce this response time is to reduce the amount of time it takes to report an accident. Smartphones are ubiquitous and with network connectivity are perfect devices to immediately inform relevant authorities about the occurrence of an accident. This paper presents the development of a system that uses smartphones to automatically detect and report car accidents in a timely manner. Data is continuously collected from the smartphone's accelerometer and analyzed using Dynamic Time Warping (DTW) to determine the severity of the accident, reduce false positives and to notify first responders of the accident location and owner's medical information. In addition, accidents can be viewed on the smartphone over the Internet offering instant and reliable access to the information concerning the accident. By implementing this application and adding a notification system, the response time required to notify emergency responders of traffic accidents can reduce the response time and perhaps help in reducing fatalities.

[1]  R. Jafari,et al.  Body sensor networks for driver distraction identification , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[2]  Dong Xuan,et al.  Mobile phone based drunk driving detection , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[3]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[4]  P. Caselli,et al.  Classification of Motor Activities through Derivative Dynamic Time Warping applied on Accelerometer Data , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Toshiyo Tamura,et al.  A Wearable Airbag to Prevent Fall Injuries , 2009, IEEE Transactions on Information Technology in Biomedicine.

[6]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Douglas C. Schmidt,et al.  WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones , 2011, Mob. Networks Appl..

[8]  Daniel Lemire,et al.  Faster retrieval with a two-pass dynamic-time-warping lower bound , 2008, Pattern Recognit..

[9]  Yufeng Jin,et al.  Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier , 2009, IEEE Sensors Journal.

[10]  Jani Mäntyjärvi,et al.  Enabling fast and effortless customisation in accelerometer based gesture interaction , 2004, MUM '04.

[11]  K. Samsudin,et al.  Evaluation of fall detection classification approaches , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

[12]  Jia-Jin Le,et al.  Similarity Search Over Data Stream using LPC-DTW , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  Li Chen,et al.  A wearable real-time fall detector based on Naive Bayes classifier , 2010, CCECE 2010.

[15]  Ram Dantu,et al.  Safe Driving Using Mobile Phones , 2012, IEEE Transactions on Intelligent Transportation Systems.

[16]  Zhen Wang,et al.  uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications , 2009, PerCom.

[17]  Stefan Madansingh,et al.  Smartphone based fall detection system , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[18]  Adisorn Tuantranont,et al.  Wireless black box using MEMS accelerometer and GPS tracking for accidental monitoring of vehicles , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[19]  Juan-Carlos Cano,et al.  Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones , 2011, 2011 IEEE 36th Conference on Local Computer Networks.

[20]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.