Fall Disclosure in Homes of Older Adults Using Kinect

Fall Disclosure in Homes of Older Adults Using Kinect

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-45 Number-3
Year of Publication : 2017
Authors : Sargunavathi.S, Harish.M, Jagan.D, Kiran.S.S
DOI :  10.14445/22315381/IJETT-V45P235

Citation 

Sargunavathi.S, Harish.M, Jagan.D, Kiran.S.S " Fall Disclosure in Homes of Older Adults Using Kinect ", International Journal of Engineering Trends and Technology (IJETT), V45(3),161-167 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
A module for recognizing decline in the home of older people using Microsoft Kinect and a two stage decline recognizer system is presented. The first stage of recognizing system distinguish a person’s steep event in particular depth image frames, and then fragments on ground events from the steep event time series capture by tracking over time. To calculate a confidence that a fall occurs on ground event the second stage uses the ensemble of decision trees. Evaluation was conducted by using the data set consists of 454 falls performed by professional stuntman .These various data collection allows the characterization of performance of the system under real time conditions to a new level. These results including the standing, lying down, near or far fall locations. By using this method better results are achieved.

 References

[1] Tran Tri Dang, Hai Truong, Tran Khanh Dang, “Automatic Fall Detection using Smartphone Acceleration Sensor” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 12,pp.123-129,2016.
[2] Goh Yongli, Ooi Shih Yin and Pang Ying Han, “State of the Art: A Study on Fall Detection” World Academy of Science, Engineering and Technology 62,pp.294-298, 2012.
[3] Sharwari Kulkarni*, MainakBasu, “A Review on Wearable Tri-Axial Accelerometer Based Fall Detectors” Journal of Biomedical Engineering and Technology, Vol. 1, No. 3, pp.36-39,2013.
[4] A.K. Bourke, G.M. Lyons, “A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor” University of Limerick, Limerick, Ireland,pp84-90,Received 5 September 2006; received in revised form 28 November 2006; accepted 3 December 2006.
[5] Henry Rimminen, JuhaLindstr¨om, MattiLinnavuo, and RaimoSepponen,“Detection of Falls Among the Elderly by a Floor Sensor Using the Electric Near Field” IEEE Transaction On Information Technology In Biomedicine,VOL.14, NO. 6,pp.1475-1476,2010.
[6] ÁgostonSrp, Dr. FerencVajda,“ Fall Detection System for Older People” Budapest University of Technology and Economics 1 ’Eurostat 2008, EUROPOP2008, convergence scenario’.
[7] Lei Yang 1, Yanyun Ren 1, Huosheng Hu 2 and Bo Tian 3,* , “ New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images” Sensors15,pp. 23004-23019,2015.
[8] Thomas Degen, Heinz Jaeckel, Michael Rufer, Stefan Wyss, “SPEEDY: A Fall Detector in a Wrist Watch” Swiss Federal Institute of Technology Electronics Laboratory 8092 Z¨urich, Switzerland {degen,jaeckel}@ife.ee.ethz.ch.
[9] ChokemongkolNadee, KosinChamnongthai,“Ultrasonic Array Sensors for Monitoring of Human Fall Detection” King Mongkut`s University of Technology Thonburi Bangkok, Thailand This email address is being protected from spambots. You need JavaScript enabled to view it.,978-1-4799-7961-5/15.
[10] Patrick Scholten,.Lettele (NL); Harry BernardllsAntonlusKerver, Duwen(NL),“FALL DETECTION ALGORITHM UTILIZING 5,865,760 A 2/ 1999 Lidman et al.A THREE_AXIS ACCELEROMETER”US pattern Scholten et al,Us8,814,81 1 B2,Aug.26,2014.
[11] M. Mathie, J. Basilakis, and B.G. Celler, “A system for monitoring posture and physical activity using accelerometers,” 23rd Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, 2001.
[12] R. Salleh, D. MacKenzie, M. Mathie, and B.G. Celler, “Low power tri-axial ambulatory falls monitor,” Proc. 10th Int. Conf. on Biomedical Engineering, 2000.
[13] M.J. Mathie, A.C. Coster, N.H. Lovell, and B.G. Celler, “Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement,” Physiol. Meas.25(2):R1- 20,2004.
[14] K. Doughty, R. Lewis, and A. McIntosh, “The design of a practical and reliable fall detector for community and institutional telecare,” J. Telemed. Telecare 6 S150-4,2000.
[15] J.A. Stevens, P.S. Corso, E.A. Finkelstein, and T.R. Miller, “The costs of fatal and non-fatal falls among older adults,” Injury Prevention, 12(5):290-295, 2006.
[16] M.ETinetti, W.L. Liu, and E.B. Claus, “Predictors and prognosis of inability to get up after falls among elderly persons," JAMA: the journal of the American Medical Association, 269(1): 65-70, 1993.
[17] G. Demiris et al., “Older Adults` Attitudes Towards and Perceptions of Smart Home Technologies: A Pilot Study,” Informatics for Health and Social Care, 29(2):87-94, 2004.
[18] Y. Zigel, D. Litvak, and I. Gannot, “A method for automatic fall detection of elderly people using floor vibrations and sound—Proof of concept on human mimicking doll falls,” IEEE Trans. Biomed. Eng., 56(12):2858–2867, 2009.
[19] D. Anderson, R.H. Luke, J. Keller, M. Skubic, M. Rantz, and M. Aud, “Linguistic summarization of activities from video for fall detection using voxel person and fuzzy logic,” Computer Vision and Image Understanding, 113(1):80–89, 2009.
[20] E. Auvinet et al., “Fall detection with multiple cameras: An occlusion resistant method based on 3-d silhouette vertical distribution," IEEE Trans. on Info. Tech. in Biomedicine, 15(2):290-300, 2011.

Keywords
Accelerator, Depth sensor, fall detection, ground event, Kinect.