IJMTES – SMART HOME TECHNOLOGY FOR ELDERLY PERSONS

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : SMART HOME TECHNOLOGY FOR ELDERLY PERSONS

Author’s Name : Mrs Kavitha K J | SriRaksha M | Shaheen Shekh | Nayana D S
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Volume 04 Issue 05 2017

ISSN no:  2348-3121

Page no: 123-126

Abstract – Various fall-detection 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. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, heart beat sensor, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate.

Keywords – Wireless Sensor Networks, Fall Detection System, Heart Rate Fluctuation ,ECG, Microcontroller, GPS, GSM.

References

  1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensornetworks: a survey,” Journal of Computer Networks, vol. 38, no. 4, pp. 393-422, March 2002.
  2. J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,”Journal of Computer Networks, vol. 52, no. 12, pp. 2292-2330, Aug. 2008.
  3. K. Kinsella and D. R. Phillips, “Global aging: the challenge of success,” Population Bulletin, vol. 60, 2005.
  4. Tabulation on the 2010 population census of the people’s republic of China, China Statistics, May 2013, on-line.
  5. S. Demura, S. Shin, S. Takahashi, and S. Yamaji, “Relationships between gait properties on soft surfaces, physical function, and fall risk for the elderly,” Advances in Aging Research, vol. 2, pp. 57-64, May 2013
  6. 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.
  7. WHO, “The injury chart-book: a graphical overview of the global burden of injury,” Geneva: WHO, pp. 43-50, 2012.
  8. 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.
  9. 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.
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