Fall Detection Using Smartphone-Based Application

Year 2016, Volume: 4 Issue: 4, - , 30.12.2016

Kerim Yildirim , Gokhan Ucar , Tahir Keskin Adnan Kavak

Abstract

Among the elderly persons and disabled citizens injuries caused by falls can be dangerous even leading to death. Fast response can improve the people’s outcome but without knowing the accident nobody can help them. Falls are more dangerous when accidents happen while the people are alone. So it is very important to inform acquaintance and caregivers at the situation like this. Our purpose here is to inform caregivers by using a device an android-based smart phone which is available everywhere or can be get easily. The method is depends on the tri-axial accelerometer integrated with the phone. The android application -evaluated with threshold based algorithm- captures data from the accelerometer when the fall occurs. Then informs the caregivers which is defined before in the settings. The algorithm first asks the user and waits for a dedicated time if he/she is all right. If he/she is not responding the application will send an e-mail, make a phone call to inform the caregivers etc…

References

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• Tideiksaar R (1998) Falling in old age: prevention and management, 2nd edn. Springer, Berlin
• Department of Health and Human Services. Fatalities and injuries from falls among older adults - united states, 1993-2003 and 2001-2005. pages 1221–1224, November 2006. Morbidity and Mortality Weekly Report.
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• Caroline Rougier and Jean Meunier. Demo: Fall detection using 3d head trajectory extracted from a single camera video sequence.
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• Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong Xuan (2010), Mobile phone-based pervasive fall detection, Pers Ubiquit Comput, DOI 10.1007/s00779-010-0292-x, Springer-Verlag London Limited 2010.
• Yavuz, G., Kocak, M., Ergun, G., Alemdar, H. O., Yalcin, H., Incel, O. D., & Ersoy, C. (2010, November). A smartphone based fall detector with online location support. In International Workshop on Sensing for App Phones; Zurich, Switzerland (pp. 31-35).
• Yabo Cao, Yujiu Yang, WenHuang Liu (2012), E-FallD : A Fall Detection System Using Android-Based Smartphone, 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).
• Carlo Tacconi, Sabato Mellone, Lorenzo Chiari,(2011) Smartphone-Based Applications for Investigating Falls and Mobility, 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.
• https://play.google.com/store/apps/details?id=
• at.spantec.securemotion.falldetector.
• https://play.google.com/store/apps/details?id=com.ec.falldetection.
• Duthie E (1989) Falls. Med Clin North Am 73:1321–1335
• Tideiksaar R (1998) Falling in old age: prevention and management, 2nd edn. Springer, Berlin
• Department of Health and Human Services. Fatalities and injuries from falls among older adults - united states, 1993-2003 and 2001-2005. pages 1221–1224, November 2006. Morbidity and Mortality Weekly Report.
• Frank Sposaro, Gary Tyson (2009), iFall: An Android Application for Fall Monitoring and Response, 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009
• Majd Alwan, Prabhu Jude Rajendran, Steve Kell, David Mack, Siddharth Dalal, Matt Wolfe, and Robin Felder. A smart and passive floor-vibration based fall detector for elderly.
• Mihail Popescu, Yun Li, Marjorie Skubic, and Marilyn Rantz. An acoustic fall detector system that uses sound height information to reduce the false alarm rate. 30th Annual International IEEE EMBS Conference, August 2008.
• Tracy Lee and Alex Mihailidis. An intelligent emergency response system: preliminary development and testing of automated fall detection. Journal of Telemedicine and Telecare, 11(4):194–198, 2005.
• Shaou-Gang Miaou, Pei-Hsu Sung, and Chia-Yuan Huang. A customized human fall detection system using omni-camera images and personal information. pages 39–41. Proceedings of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference, April 2006.
• Hammadi Nait-Charif and Stephen J. McKenna. Activity summarization and fall detection in a supportive home environment. 2004.
• Caroline Rougier and Jean Meunier. Demo: Fall detection using 3d head trajectory extracted from a single camera video sequence.
• K Doughty, R Lewis, and A McIntosh. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare, 6(1):150–154, 2000.
• Thomas Riisgaard Hansen, J. Mikael Eklund, Jonthan Sprinkle, Ruzena Bajcsy, and Shankar Sastry. Using smart sensors and a camera phone to detect and verify the fall of elderly persons. European Medicine, Biology and Engineering Conference (EMBEC 2005), November 2005.
• Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong Xuan (2010), Mobile phone-based pervasive fall detection, Pers Ubiquit Comput, DOI 10.1007/s00779-010-0292-x, Springer-Verlag London Limited 2010.
• Yavuz, G., Kocak, M., Ergun, G., Alemdar, H. O., Yalcin, H., Incel, O. D., & Ersoy, C. (2010, November). A smartphone based fall detector with online location support. In International Workshop on Sensing for App Phones; Zurich, Switzerland (pp. 31-35).
• Yabo Cao, Yujiu Yang, WenHuang Liu (2012), E-FallD : A Fall Detection System Using Android-Based Smartphone, 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).
• Carlo Tacconi, Sabato Mellone, Lorenzo Chiari,(2011) Smartphone-Based Applications for Investigating Falls and Mobility, 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.
• https://play.google.com/store/apps/details?id=
• at.spantec.securemotion.falldetector.
• https://play.google.com/store/apps/details?id=com.ec.falldetection.