An Enhanced Fall Detection System for Elderly Person Monitoring using Consumer Home Networks

International Journal of Electronics and Communication Engineering
© 2016 by SSRG - IJECE Journal
Volume 3 Issue 10
Year of Publication : 2016
Authors : LakshmiPriyankaDevi.M, T. Ravi kumar and Girish Kumar PVR
pdf
How to Cite?

LakshmiPriyankaDevi.M, T. Ravi kumar and Girish Kumar PVR, "An Enhanced Fall Detection System for Elderly Person Monitoring using Consumer Home Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 3,  no. 10, pp. 19-22, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I10P105

Abstract:

This project describes Various falldetection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this project, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks the design of a simple, low-cost controller based wireless fetal heart beat monitoring system. Heart rate of the subject is measured from the thumb finger using IRD (Infra Red Device sensors and the rate is then averaged and displayed on a text based LCD).The device LCD displaying the heart beat rat and0 counting values through sending pulses from the sensor. This instrument employs a simple Opto electronic sensor, conveniently strapped on the finger, to give continuous indication of the pulse digits. The Pulse monitor works both on battery or mains supply. It is ideal for continuous monitoring in operation theatres, I.C.units, biomedical/human engineering studies and sports medicine. This project uses as ARM 7 (LPC2148) its controller. By reading pulse values continuously from pulse count sensor these values are displayed wirelessly using GSM technology.

Keywords:

Arm 7 (Lpc2148), Mems Sensor, Panic Switch, Temp Sensor, Heart Beat Sensor, GSM.

References:

[1] ARM Architecture Reference Manual by David Seal : Addison-Wesley
[2] ARM System-on-chip Architecture by Steve Furber.
[3] Wireless Medical Technologies: A Strategic Analysis of Global Markets [online]. International Telecoms Intelligence.
[4] G. Y. Jeong, K. H. Yu, and Kim. N. G. Continuous blood pressure monitoring using pulse wave transit time. In International Conference on Control, Automation and
[5] Systems (ICCAS), 2005.
[6] K. Hung, Y. T. Zhang, and B. Tai. Wearable medical devices for telehome healthcare. In Procs. 26th Annual International Conference on the IEEE EMBS, 2004.
[7] Fang, Xiang et al: An extensible embedded terminal platform for wireless telemonitoring, Information and Automation (ICIA), 2012 International Conference on Digital Object Identifier: 10.1109/ICInfA.2012.6246761
[8] Publication Year: 2012 , Page(s): 668 – 673.
[9] Majer, L., Stopjaková, V., Vavrinský, E.: Sensitive and Accurate Measurement Environment for Continuous Bio
[10] medical Monitoring using Microelectrodes. In: Measurement Science Review. - ISSN 1335- 8871. - Vol. 7, Sec-tion 2, No. 2 (2007), s. 20-24.
[11] Majer, L., Stopjaková, V., Vavrinský, E.: Wireless Measurement System for Non-Invasive Biomedical Monitoring of PsychoPhysiological Processes. In: Journal of lectrical Engineering. - ISSN 1335-3632. - Vol. 60, No. 2 (2009), s. 57-68.
[12] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Journalof Computer Networks, vol. 52, no. 12, pp. 2292-2330,Aug. 2008.
[13] K. Kinsella and D. R. Phillips, “Global aging: the challenge of success,” Population Bulletin, vol. 60, 2005.
[14] Tabulation on the 2010 population census of the people’s republic of China, China Statistics, May 2013, on-line.
[15] S. Demura, S. Shin, S. Takahashi, and S. Yamaji,“Relationships between gait properties on soft surfaces, physical function, and fall riskfor the elderly,” Advances in Aging Research, vol. 2, pp. 57 -64, May 2013.
[16] S. R. Lord and J. Dayhew, “Visual risk factors for falls in older people,” Journal of American Geriatrics Society, vol. 49, no. 5, pp. 508-515, Dec. 2001.
[17] WHO, “The injury chart-book: a graphical overview of the global burden of injury,” Geneva: WHO, pp. 43-50, 2012.
[18] M. Mubashir, L. Shao, and L. Seed, “A survey on fall detection: Principles and approaches,” Neurocomputing, vol. 100, no. 16, pp. 144-152, Jan. 2013.
[19] Q. Zhang, L. Ren, and W. Shi, “HONEY a multimodality fall detection and telecare system,” Telemedicine and e-Health, vol. 19, no. 5, pp. 415-429, Apr. 2013.
[20] F. Bagalà, C. Becker, A. Cappello, L. Chiari,and K. Aminian, “Evaluation of accelerometer-based fall detection algorithm in realworld falls,” PLoS ONE, vol. 7,no. 5, pp. 1-8, May 2012.