Android Based Fall Detection Alert System using MultiSensor

Android Based Fall Detection Alert System using Multi-Sensor

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-46 Number-6
Year of Publication : 2017
Authors : Neha R. Singh, P.R. Rothe, A. P. Rathkanthiwar
DOI :  10.14445/22315381/IJETT-V46P252

Citation 

Neha R. Singh, P.R. Rothe, A. P. Rathkanthiwar "Android Based Fall Detection Alert System using Multi-Sensor", International Journal of Engineering Trends and Technology (IJETT), V46(6),298-304 April 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
An enhanced fall detection system is proposed for elderly person monitoring that is based on-body sensor operating through consumer home networks. The on-body sensor which consists of accelerometer and cardiotachometer is used in this model. In this proposed system accelerometer measures overall vibration by means of using Signal Magnitude Vector and trunk angle. Here Signal Magnitude Vector is used to calculate the acceleration caused due to movement of the body with respect to xyz axis and trunk angle is used to calculate the posture of the elderly person during fall event. Cardio tachometer is used to measure the pulse rate. A typical fall event ends with the person lying on the ground or leaning on walls, or furniture that will cause a significant change in trunk angle. In this case, it is desirable to consider changes on the trunk angle to detect whether the detected acceleration was due to a fall event. The set values of acceleration and pulse provides accuracy to the system avoiding false detection. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. This system is connected to GPS and GSM for communication purpose which is unique. When the fall is detected the GPS locates the exact fall location and GSM modem is used to transmit the message to the mobile phone of caretakers/relatives of the fallen subjects at that time also send their latitude and longitude value by using GPS.. This alert message helps to provide immediate assistance and treatment.

 References

[1] 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.
[2] F. Bagalà, C. Becker, A. Cappello, L. Chiari, and K. Aminian, “Evaluation of accelerometer-based fall detection algorithm in real world falls,” PLoS ONE, vol. 7, no. 5, pp. 1-8, May 2012
[3] Noury N, Rumeau P, Bourke AK, OLaighin G, Lundy JE: A proposal for the classification and evaluation of fall detectors. Irbm 2008, 29:340–349.
[4] S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and A.Vecchio, “A smartphone-based fall detection system,” Pervasive and Mobile Computing, vol. 8, no. 6, pp. 883-899, Dec. 2012.
[5] S. Abbate, M. Avvenuti, G. Cola, P. Corsini, J.V. Light, and A.Vecchio, “Recognition of false alarms in fall detection systems,” in Proc. 2011 IEEE Consumer Communications and Networking Conference, Las Vegas, USA, pp. 23-28, Jan. 2011.
[6] M. Yu, A. Rhuma, S. Naqvi, L. Wang, and J. Chambers, “A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment,” IEEE Trans. Infor. Tech. Biom., vol. 16, no. 6, pp. 1274-1286, Aug. 2012.
[7] Sazonov ES, Fulk G, Sazonova N, Schuckers S: Automatic recognition of postures and activities in stroke patients. Conf Proc IEEE Eng Med Biol Soc 2009, 2009:2200-2203.
[8]. Sposaro F, Tyson G: iFall: an Android application for fall monitoring and response. Conf Proc IEEE Eng Med Biol Soc 2009, 2009:6119-6122.
[9] Dai J, Bai X, Yang Z, Shen Z, Xuan D: PerFallD: A pervasive fall detection system using mobile phones. IEEE International Conference on Pervasive Computing and Communications Workshops; Mannheim 2010, 292-297.
[10] Yavuz G, Kocak M, Ergun G, Alemdar HO, Yalcin H, Incel OD, Ersoy C: A smart phone based fall detector with online location support. International Workshop on Sensing for App Phones; Zurich, Switzerland 2010, 31-35.
[11] Paradiso R, Alonso A, Cianflone D, Milsis A, Vavouras T, Malliopoulos C: Remote health monitoring with wearable noninvasive mobile system: the health wear project. Conf Proc IEEE Eng Med Biol Soc 2008, 1699-1702.
[12] M. Popescu, Y. Li, M. Skubic, M. Rantz, “An Acoustic Fall Detector System that Uses Sound Height Information to Reduce the False Alarm Rate”, in Proc. 30th Int. IEEE Eng. in Medicine and Bio. Soc. Conference, pp. 4628-4631, Aug.2008.
[13] J. Winkley, P. Jiang, and W. Jiang, “Verity: An Ambient Assisted Living Platform,” IEEE Trans. Consumer Electron., vol. 58, no. 2, pp. 364-373, May 2012.
[14] H.R. Yan, H.W. Huo, Y.Z. Xu, and M. Gidlund, “Wireless sensor network based E-health system: implementation and experimental results,” IEEE Trans. Consumer Electron., vol. 56, no. 4, pp. 2288-2295, Nov. 2010.
[15] Jin Wang, Member, IEEE, Zhongqi Zhang, Bin Li, Sungyoung Lee, and R. Simon Sherratt, Fellow, IEEE An Enhanced Fall Detection System for Elderly Person Monitoring using Consumer Home Networks 1, February 2014.

Keywords
Wireless Sensor Networks, Fall Detection System, Global Positioning System Accelerometer, Cardiotachometer.