Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System

Hiroyuki Kakara; Yoshifumi Nishida; Sang Min Yoon; Hiroshi Mizoguchi; Tatsuhiro Yamanaka

doi:10.20965/jrm.2012.p0802

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JRM Vol.24 No.5 pp. 802-810

doi: 10.20965/jrm.2012.p0802

(2012)

Paper:

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Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System

Hiroyuki Kakara^1,2, Yoshifumi Nishida^2, Sang Min Yoon^3,
Hiroshi Mizoguchi^1,2, and Tatsuhiro Yamanaka^*4

^*1Department of Mechanical Engineering, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan

^*2Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan

^*3School of Computer Science, Kookmin University, 861-1 Chungnung-dong, Songbuk-gu, Seoul 136-702, Korea

^*4Ryokuen Children’s Clinic, 2-1-6-201 Ryokuen, Midori-ku, Yokohama-shi, Kanagawa 245-0002, Japan

Received:

March 1, 2012

Accepted:

July 4, 2012

Published:

October 20, 2012

Keywords:

childhood injury prevention, daily behavior, fall database, fall dynamics

Abstract

This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algorithm was developed to automate fall motion analysis. Using the CV algorithm, fall motion data were accumulated into a database. The database allows a user to perform conditional searches for fall data by inputting search conditions, such as a child’s attributes, and fall situations.

Cite this article as:

H. Kakara, Y. Nishida, S. Yoon, H. Mizoguchi, and T. Yamanaka, “Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System,” J. Robot. Mechatron., Vol.24 No.5, pp. 802-810, 2012.

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References

[1] National Institute of Advanced Industrial Science and Technology, “Project of Building a Safety-Knowledge-Recycling-Based Society 2009,” 2009 (in Japanese).
[2] T. R. Miller, E. O. Romano, and R. S. Spicer, “The cost of childhood unintentional injuries and the value of prevention,” The Future of Children (issue on Unintentional injuries in childhood), Vol.10, pp. 137-163, 2000.
[3] World Health Organization, “World Report on Child Injury Prevention,” M. Peden, K. Oyegbite, J. Ozanne-Smith, A. A. Hyder, C. Branche, A. F. Rahman, F. Rivara, and K. Bartolomeos (Eds.), 2008.
[4] Y. Miyazaki, Y. Murai, Y. Nishida, T. Yamanaka, M. Mochimaru, and M. Kouchi. “Head Injury Analysis In Case of Fall from Playground Equipment Using Child Fall Simulator,” The impact of Technology on Sport, Vol.3, pp. 417-421, 2009.
[5] Y. Miyazaki, Y. Nishida, Y. Motomura, T. Yamanaka, and I. Kakefuda, “Computer Simulation of Childhood Head Injury Due To Fall From Playground Equipment,” The 2nd Asia Pacific Injury Prevention Conference, November 2008.
[6] A. K. Bourke, J. V. O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm,” Gait and Posture, Vol.26, Issue 2, pp. 194-199, November 2006.
[7] T. Zhang, J. Wang, P. Liu, and J. Hou, “Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm,” IJCSNS Int. J. of Computer Science and Network Security, Vol.6, No.10, pp. 277-284, October 2006.
[8] S. Robinovitch, F. Feldman, D.Wan, O. Aziz, and T. Sarraf, “Video recording of real-life falls in long term care provides new insight on the cause and circumstances offalls in older adults,” Proc. of the XIX Conf. of the Int. Society for Posture & Gait Research Bologna, p. 80, June 2009.
[9] M. J. Gibson et al., “Falls in later life,” Improving the Health of Older People: a World View. New York: Oxford University Press, pp. 296-315, 1990.
[10] H. Kakara, Y. Nishida, Y. Miyazaki, H. Mizoguchi, and T. Yamanaka, “Development of Database of Fall Dynamics Probabilistic Distribution for Biomechanical Simulation,” The Proc. of The 2011 Int. Conf. on Modeling, Simulation and Visualization Methods, pp. 82-88, July 19, 2011

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] National Institute of Advanced Industrial Science and Technology, “Project of Building a Safety-Knowledge-Recycling-Based Society 2009,” 2009 (in Japanese).

[2] [2] T. R. Miller, E. O. Romano, and R. S. Spicer, “The cost of childhood unintentional injuries and the value of prevention,” The Future of Children (issue on Unintentional injuries in childhood), Vol.10, pp. 137-163, 2000.

[3] [3] World Health Organization, “World Report on Child Injury Prevention,” M. Peden, K. Oyegbite, J. Ozanne-Smith, A. A. Hyder, C. Branche, A. F. Rahman, F. Rivara, and K. Bartolomeos (Eds.), 2008.

[4] [4] Y. Miyazaki, Y. Murai, Y. Nishida, T. Yamanaka, M. Mochimaru, and M. Kouchi. “Head Injury Analysis In Case of Fall from Playground Equipment Using Child Fall Simulator,” The impact of Technology on Sport, Vol.3, pp. 417-421, 2009.

[5] [5] Y. Miyazaki, Y. Nishida, Y. Motomura, T. Yamanaka, and I. Kakefuda, “Computer Simulation of Childhood Head Injury Due To Fall From Playground Equipment,” The 2nd Asia Pacific Injury Prevention Conference, November 2008.

[6] [6] A. K. Bourke, J. V. O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm,” Gait and Posture, Vol.26, Issue 2, pp. 194-199, November 2006.

[7] [7] T. Zhang, J. Wang, P. Liu, and J. Hou, “Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm,” IJCSNS Int. J. of Computer Science and Network Security, Vol.6, No.10, pp. 277-284, October 2006.

[8] [8] S. Robinovitch, F. Feldman, D.Wan, O. Aziz, and T. Sarraf, “Video recording of real-life falls in long term care provides new insight on the cause and circumstances offalls in older adults,” Proc. of the XIX Conf. of the Int. Society for Posture & Gait Research Bologna, p. 80, June 2009.

[9] [9] M. J. Gibson et al., “Falls in later life,” Improving the Health of Older People: a World View. New York: Oxford University Press, pp. 296-315, 1990.

[10] [10] H. Kakara, Y. Nishida, Y. Miyazaki, H. Mizoguchi, and T. Yamanaka, “Development of Database of Fall Dynamics Probabilistic Distribution for Biomechanical Simulation,” The Proc. of The 2011 Int. Conf. on Modeling, Simulation and Visualization Methods, pp. 82-88, July 19, 2011

Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System

Hiroyuki Kakara*1,*2, Yoshifumi Nishida*2, Sang Min Yoon*3, Hiroshi Mizoguchi*1,*2, and Tatsuhiro Yamanaka*4

Hiroyuki Kakara^1,2, Yoshifumi Nishida^2, Sang Min Yoon^3,
Hiroshi Mizoguchi^1,2, and Tatsuhiro Yamanaka^*4