Smartphone-based mobile gunshot detection

The ability to detect gunshots can provide someone with invaluable information in various circumstances. For the military and public servants, detecting gunshots can help save lives and potentially target offenders. People participating in shooting sports as beginners or professionals can also benefit from the use of sensors for improving their reaction and self control during training. Most current methods for gunshot detection require expensive devices that are purpose built or developed and often only examine one or two features of the gunshots such as the sound, recoil, or visible flash. Using the current sensors built into smartphones, 15 samples utilizing 10 different sensors are used examine how gunshot detection can be performed through the use of simple sensors. This extension of human activity recognition resulted in gunshot classification accuracy ranging from 0.0% – 99.7% with an average of 86.6%. Understanding how simple sensors respond to gunshots can provide simple and easy accessibility to new detection methods and ample opportunities to improve this potential field for various personal and smart city applications such as crime detection, policing, gunshot violence monitoring and control in the community.

[1]  Pablo Alvarado-Moya,et al.  Evaluation of gunshot detection algorithms , 2008, 2008 Argentine School of Micro-Nanoelectronics, Technology and Applications.

[2]  Parth H. Pathak,et al.  AccelWord: Energy Efficient Hotword Detection through Accelerometer , 2015, MobiSys.

[3]  Robert C. Maher,et al.  Acoustical Characterization of Gunshots , 2007 .

[4]  Jun Yang,et al.  Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[5]  Henry T. Nagamatsu,et al.  An Experimental Study of Perforated Muzzle Brakes , 1984 .

[6]  Shuangquan Wang,et al.  Unobtrusive Sensing Incremental Social Contexts Using Fuzzy Class Incremental Learning , 2015, 2015 IEEE International Conference on Data Mining.

[7]  R.C. Maher,et al.  Modeling and Signal Processing of Acoustic Gunshot Recordings , 2006, 2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop.

[8]  Jiaquan Xu,et al.  Deaths: Final Data for 2014. , 2016, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[9]  Adil Mehmood Khan,et al.  Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs , 2014, Int. J. Distributed Sens. Networks.

[10]  John Nelson,et al.  Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices , 2014, Sensors.

[11]  Seok-Won Lee,et al.  Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones , 2013, Sensors.

[12]  Parth H. Pathak,et al.  Monitoring building door events using barometer sensor in smartphones , 2015, UbiComp.