A Smartphone-based Fall Detection System using Accelerometer and Microphone

Shakeri, Saeid

doi:10.22041/ijbme.2017.40697.1119

Document Type : Technical note

Author

Saeid Shakeri

Ph.D Student, Bioelectric Department, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran

https://doi.org/10.22041/ijbme.2017.40697.1119

Abstract

Falls are one of the main reasons to injury, especially in the elderly people. These injuries can be reduced by quick and accurate response or reaction, but this is not possible often in elderly people because they usually live alone and after injury caused the falling, cannot call for help. This paper presents a fall detection system to do twomajor tasks properly and quickly; firstly, it shoulddetect fall from other daily activities andsecondly, transmit falling person’s necessary information to help. This system is implemented on Android-based smartphone and it used tri-axial accelerometer and microphone to fall detection. Everydayinteraction with the smartphone makes our system more familiarto the user. The accelerometer is used to record variations of acceleration in three directions.Thissystem isimproved with detecting the noise caused the falling, by analyzing environmental sounds. After fall detection, a warning text message that contains information about time and location of the falling will besent to the caregivers. A comprehensive evaluation with 18 volunteers shows that the proposed system has sensitivity of 96% and specificity of 77% for different types of fall in quiet and noisy environments.

Keywords

Main Subjects

Bioelectrics

References

[1] Aslan, U. B. Cavlak, U. Yagci, N.& Akdag, B.“Balance performance, aging and falling: a comparative study based on a Turkish sample”. Archives of gerontology and geriatrics, 46(3), 283-292, 2008.

[2] Laughton, C. A., Slavin, M., Katdare, K., Nolan, L., Bean, J. F., Kerrigan, D. C& Collins, J. J. “Aging, muscle activity, and balance control: physiologic changes associated with balance impairment”. Gait & posture, 18(2), 101-108, 2003.

[3] Lee, D. T. “Quality long‐term care for older people: a commentary”. Journal of Advanced nursing, 52(6), 618-619, 2005.

[4] Mohtasham Amiri Z, Toloei MH, Farazmand E. “Causes of patients’hospitalization in Guilan university hospitals”.J Med Fac Guilan Univ Med Sciences, 42(11), 28-32,2002.

[5] مرکز آمار ایران، گزیده نتایج سرشماری عمومی نفوس و مسکن سال 1390، چاپ اول، صفحات 24-28، مرداد 1391،

Available: http://www.amar.org.ir

[6] Z Safavi Bayat, F Zorriasatain“Determining risk factors associated with falling among elderly at residential care facilities in Tehran”. Journal of Qazvin Univ. of Med. Sci. Vol. 11, No. 4, 66-70, 2008.

[7] Sh.SalarVand, M.Birjandi,“Factors Related to Falling Down in Older Adults”. Iran journal of Nursing (IJN), Vol. 22 , No. 61; 51- 60. 2009.

[8] Perry, J. T., Kellog, S., Vaidya, S. M., Youn, J. H., Ali, H., & Sharif, H. “Survey and evaluation of real-time fall detection approaches”. In High-Capacity Optical Networks and Enabling Technologies (HONET), 6th International Symposium on (pp. 158-164).IEEE, 2009.

[9] Cavoukian, A., A. Mihailidis, and J. Boger. “Sensors and in-home collection of health data: A privacy by design approach”. Information and Privacy Commissioner, Tech. Rep, 2010.

[10] Igual, R., Medrano, C., & Plaza, I. “Challenges, issues and trends in fall detection systems”. Biomed.Eng. Online, 12(66), 2013.

[11] Sposaro, Frank, and Gary Tyson. “iFall: an Android application for fall monitoring and response.” Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE., 2009.

[12] Dai J, Bai X, Yang Z, Shen Z, Xuan D“Mobile phone-based pervasive fall detection.” Pers Ubiquitous Comput 2010,14:633–643, 2010.

[13] Albert MV, Kording K, Herrmann M, Jayaraman A “Fall classification by machine learning using mobile phones”. PLoS One 2012, 7:e36556, 2012.

[14] Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. “A smartphone-based fall detection system”. Pervasive and Mobile Computing, 8(6), 883-899, 2012.

[15] Hsieh, S. L., Chen, K. R., Yeh, C. L., & Chen, C. C. “An unfixed-position smartphone-based fall detection scheme”. In Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on (pp. 2077-2081), 2014.

[16] ShenVictor RL, Horng-Yih Lai, and Ah-Fur Lai.“The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net.”Applied Soft Computing 26, 390-400, 2015.

[17] Lee, Raymond YW, and Alison J. Carlisle. “Detection of falls using accelerometers and mobile phone technology.” Age and ageing, 2011.

[18] Fang, Shih-Hau, Yi-Chung Liang, and Kuan-Ming Chiu. "Developing a mobile phone-based fall detection system on Android platform." Computing, Communications and Applications Conference (ComComAp), 2012.IEEE, 143–146, 2012.

[19] Bourke, A. K., O’donovan, K. J., & Olaighin, G. “ The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls”. Medical Engineering & Physics, 30(7), 937-946,2008.

[20] Habib, M. A., Mohktar, M. S., Kamaruzzaman, S. B., Lim, K. S., Pin, T. M., & Ibrahim, F. “Smartphone-based solutions for fall detection and prevention: challenges and open issues”. Sensors, 14(4), 7181-7208, 2014.

[21] Sprute, D., Pörtner, A., Weinitschke, A., & König, M. “Smart Fall: Accelerometer-Based Fall Detection in a Smart Home Environment.” InInclusive Smart Cities and e-Health, Springer International Publishing, 194-205, 2015.

[22] Kozina, S., Gjoreski, H., Gams, M., & Luštrek, M. “Efficient activity recognition and fall detection using accelerometers”. In Evaluating AAL Systems Through Competitive Benchmarking, Springer Berlin Heidelberg, 13-23, 2013.

Iranian Journal of Biomedical Engineering

A Smartphone-based Fall Detection System using Accelerometer and Microphone

References

References

Volume 9, Issue 4
February 2015
Pages 399-410

A Smartphone-based Fall Detection System using Accelerometer and Microphone

References

References

Volume 9, Issue 4February 2015Pages 399-410

Volume 9, Issue 4
February 2015
Pages 399-410