Abstract
Falls is a main risk for the elderly man living without assistance. Rapid and fall detection events can reduce the rate of Humanity and raise the chances to survive and independent living of old person. In the last few decades, several technological and medical treatment solutions for detection of falls were published, but most of them suffer from vital limitations. We will discuss the various challenges and concept related to fall detection system for elderly person, A lot of research being conducted is being aimed at finding solutions for helping the elderly and their caretakers in case of incidences of falls; fall detection mechanism and notification alarms in case of falls have been developed in order to reduce fall consequences. Recently, the medical and behavioral history of elderly patients has also been taken into account for predicting the possibility of falls and devising better fall likelihood prediction systems.
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References
Kinsella, K., Phillips, D.R.: Global aging: the challenge of success. Popul. Bull. 60, 3 (2005)
Tabulation on the 2010 population census of the people’s republic of China, China Statistics, May 2013. On-line
Popescu, M., Li, Y., Skubic, M., Rantz, M.: An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In: Proceedings of 30th International IEEE Engineering in Medicine and Biology Society Conference, pp. 4628–4631, August 2008
Zhang, Q., Ren, L., Shi, W.: HONEY a multimodality fall detection and telecare system. Telemed. e-Health 19(5), 415–429 (2013)
Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., Vecchio, A.: A smartphone-based fall detection system. Pervasive Mob. Comput. 8(6), 883–899 (2012)
Bagalà , F., Becker, C., Cappello, A., Chiari, L., Aminian, K.: Evaluation of accelerometer-based fall detection algorithm in real-world falls. PLoS ONE 7(5), 1–8 (2012)
Bai, Y.W., Wu, S.C., Tsai, C.L.: Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone. IEEE Trans. Consum. Electron. 58(4), 1269–1275 (2012)
Yu, M., Rhuma, A., Naqvi, S., Wang, L., Chambers, J.: A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 16(6), 1274–1286 (2012)
Rougier, C., Meunier, J., Arnaud, A.S., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circ. Syst. Video Technol. 21(5), 611–622 (2011)
Ozcan, K., Mahabalagiri, A., Velipasalar, S.: Automatic fall detection and activity classification by a wearable camera. In: Bobda, C., Velipasalar, S. (eds.) Distributed Embedded Smart Cameras, pp. 151–172. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7705-1_7
Yan, H.R., Huo, H.W., Xu, Y.Z., Gidlund, M.: Wireless sensor network based E-health system: implementation and experimental results. IEEE Trans. Consum. Electron. 56(4), 2288–2295 (2010)
Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. Biomed. Eng. Online 12(1), 66 (2013)
Rajagopalan, R.: Smart and pervasive health systems—challenges, trends, and future directions. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 69, pp. 408–419. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12388-8_29
Ding, W., Jing, X., Yan, Z., Yang, L.T.: A survey on data fusion in internet of things: towards secure and privacy-preserving fusion. Inf. Fusion 51, 129–144 (2019)
Stone, E.E., Skubic, M., Rantz, M., Popescu, M.:Â U.S. Patent Application No. 16/108,432 (2019)
Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf. Fusion 35, 68–80 (2017)
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, 7181–7208 (2014)
Webster, D., Celik, O.: Systematic review of Kinect applications in elderly care and stroke rehabilitation. J. Neuroeng. Rehabil. 11, 108 (2014)
O’Sullivan, M., Blake, C., Cunningham, C., Boyle, G., Finucane, C.: Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age Ageing 38, 308–313 (2009)
Greene, B.R., O’Donovan, A., Romero-Ortuno, R., Cogan, L., Scanaill, C.N., Kenny, R.A.: Quantitative falls risk assessment using the timed up and go test. IEEE Trans. Biomed. Eng. 57, 2918–2926 (2010)
Allen, N.E., Schwarzel, A.K., Canning, C.G.: Recurrent falls in Parkinson’s disease: a systematic review. Parkinson’s Dis. 2013, 906274 (2013)
Berg, K., Wood-Dauphine, S., Williams, J.I., Gayton, D.: Measuring balance in the elderly: preliminary development of an instrument. Physiother. Can. 41, 304–311 (1989)
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Aggrawal, R.K., Gahlan, M. (2020). Distinctive Type of Fall Detection Methods Using Wearable Device Safety and Security of Elderly Person. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_35
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DOI: https://doi.org/10.1007/978-981-15-5827-6_35
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