Smartphone Based Fall Detection and Logic Testing Application Using Android SDK


Smart systems aimed at detecting the fall of a person have increased significantly due to recent technological advances and availability of modular electronics. This work presents the use of embedded accelerometer and gyroscope in mobile phones to accurately detect and classify the type of fall a person is experiencing before suffering an impact. Early classification of fall type helps in optimizing the algorithm of the fall detection. User acceptance, feasibility and the limitations in the accuracy of the existing devices have also been considered in this study. High efficiency and low power approaches were emphasized with wireless capability that enhanced the system performance for variety of applications. There is a need of reducing the time for analyzing the smart algorithms designed. It is also emphasized that this application will be a good platform that can be used to test various algorithms and multiple sensors at a time with ease and obtain data analysis in a short period.

Share and Cite:

Suryadevara, V. and Rizkalla, M. (2015) Smartphone Based Fall Detection and Logic Testing Application Using Android SDK. Journal of Biomedical Science and Engineering, 8, 616-624. doi: 10.4236/jbise.2015.89057.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Sposaro, F. and Tyson, G. (2009) iFall: An Android Application for Fall Monitoring and Response. Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, In: Maxwell, J.C., A Treatise on Electricity and Magnetism, 3rd Edition, vol. 2. Clarendon, Oxford, 1892, 68-73.
[2] Dai, J., Bai, X., Yang, Z., Shen, Z. and Xuan, D. (2010) PerFallD: A Pervasive Fall Detection System using Mobile Phones. Proceedings of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Mannheim, 29 March-2 April 2010, 292-297.
[3] Igual, R., Medrano, C. and Plaza, I. (2012) Challenges, Issues and Trends in Fall Detection Systems. Biomedical Engineering Online, 12, 66.
[4] Albert, M.V., et al. (2012) Fall Classification by Machine Learning Using Mobile Phones. PloS One, 7, e36556.
[5] Tacconi, C., Mellone, S. and Chiari, L. (2011) Smartphone-Based Applications for Investigating Falls and Mobility. Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, 23-26 May 2011, 258-261.
[6] Mehner, S., Klauck, R. and Koenig, H. (2013) Location-Independent Fall Detection with Smartphone. Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments, Rhodes, 28-31 May 2013, Article No. 11.
[7] He, Y., Li, Y. and Yin, C. (2012) Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS). E-Health Telecommunication Systems and Networks, 1, 1-5.
[8] Sposaro, F. and Tyson, G. (2009) iFall: An Android Application for Fall Monitoring and Response. Proceedings of the Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), Minneapolis, MN, 3-6 September 2009, 6119-6122.
[9] Lee, J.-V., Chuah, Y.-D. and Chieng, K.T.H. (2013) Smart Elderly Home Monitoring System with an Android Phone. International Journal of Smart Home, 7, 17-32.
[10] Hansen, T.R., Eklund, J.M., Sprinkle, J., Bajcsy, R. and Sastry, S. (2005) Using Smart Sensors and a Camera Phone to Detect and Verify the Fall of Elderly Persons. Proceedings of the European Medicine, Biology and Engineering Conference, Prague, 20-25 November 2005.
[11] Yavuz, G., Kocak, M., Ergun, G., Alemdar, H., Yalcin, H., Incel, O.D. and Ersoy, C. (2010) A Smartphone Based Fall Detector with Online Location Support. Proceedings of the International Workshop on Sensing for App Phones, Zurich, 2 November 2010, 31-35.
[12] Castillo, J.C., Carneiro, D., Serrano-Cuerda, J., Novais, P., Fernández-Caballero, A. and Neves, J. (2013) A Multi-Modal Approach for Activity Classification and Fall Detection. International Journal of Systems Science, 45, 810-824.
[13] Bai, Y.-W., Wu, S.-C. and Yu, C.H. (2013) Recognition of Direction of Fall by Smartphone. Proceedings of the 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, SK, 5-8 May 2013, 1-6.
[14] Shi, Y., Shi, Y.C. and Wang, X. (2012) Fall Detection on Mobile Phones Using Features from a Five-Phase Model. Proceedings of the 9th International Conference on Ubiquitous Intelligence and Computing, and Autonomic and Trusted Computing, Fukuoka, 4-7 September 2012, 951-956.
[15] Werner, F., Diermaier, J., Panek, P. and Schmid, S. (2011) Fall Detection with Distributed Floor-Mounted Accelerometers: An Overview of the Development and Evaluation of a Fall Detection System within the Project eHome. Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, 23-26 May 2011, 354-361.
[16] Sorvala, A., Alasaarela, E., Sorvoja, H. and Myllyla, R. (2012) A Two-Threshold Fall Detection Algorithm for Reducing False Alarms. Proceedings of the 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, CA, 25-29 March 2012, 1-4.
[17] Guyon, I. and Elisseeff, A. (2006) An Introduction to Feature Extraction. In: Guyon, I., Nikravesh, M., Gunn, S. and Zadeh, L., Eds., Feature Extraction, Springer Berlin Heidelberg, Zurich, Volume 207, 1-25.
[18] Amick, R.Z., Patterson, J.A. and Jorgensen, M.J. (2013) Sensitivity of Tri-Axial Accelerometers within Mobile Consumer Electronic Devices: A Pilot Study. International Journal of Applied Science and Technology, 3, 97-100.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.