A Healthy Monitor System for Fall and Balance Detection of Elderly

Author(s): 
Z. J. Hou†*, Z. Q. Lu†, J. Z. Liang†, C. Chen‡, & Y. Xu†

Affiliation(s): 
†School of Information Science & Engineering, Changzhou University, Changzhou, 213164, China,
‡Department of Electrical Engineering, University of Texas at Dallas, Richardson, Texas, USA

Cite this paper
Z. J. Hou†*, Z. Q. Lu†, J. Z. Liang†, C. Chen‡, & Y. Xu†, “A Healthy Monitor System for Fall and Balance Detection of Elderly”, Journal of Mechanical Engineering Research and Developments, vol. 39, no. 2, pp. 364-372, 2016. DOI: 10.7508/jmerd.2016.02.012

ABSTRACT: This paper presents a non wearable health monitor system for elderly fall detection and balance ability evaluation. To protect the privacy of the elderly, the system uses a Microsoft Kinect depth camera to capture the depth images and then construct the human 3D action box. Different features extracted from the 3D action box are used as input to a BP neural network classifier for fall detection. This system can monitor the daily life of the elderly. When the elderly fall, the system will alarm. Meanwhile it can detect the balance ability for the elderly to prevent them from getting senile dementia. Experiments show that the system’s accuracy has reached 90% which is robust and reliable.

Keywords : Fall detection; Balancing; Kinect; BP neural network.

 

References

[1] W. H. O. Ageing and L. C. Unit, WHO global report on falls prevention in older age. World Health Org., Geneva, Switzerland, 2008.
[2] Cook, A.M.: The future of assistive technologies: A time of promise and apprehension. In: Proc. of the 12th Int. ACM SIGACCESS Conf.on Comp. and Accessibility, ACM, New York, USA (2010) 1–2.
[3] Buesching, F., Kulau, U., Gietzelt, M., Wolf, L.: Comparison and validation of capacitive accelerometers for health care applications.Comp. Methods and Programs in Biomedicine 106(2), (2012)79–88. 
[4] Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26(2), (2007)194–199.
[5] Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle,V., Lundy, J.E.: Fall detection – principles and methods. In: IEEE Int.Conf. on Eng. in Medicine and Biology Society, (2007)1663–1666
[6] Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics 30(1), (2008)84–90. 
[7] Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: SPEEDY: A fall detector in a wrist watch. In: Proc. of the 7th IEEE Int. Symp. on Wearable Comp., p. 184. IEEE Computer Society, Washington, DC, USA, 2003.
[8] Popescu, M., Li, Y., Skubic, M., Rantz, M.: An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In: IEEE International Conference of the Engineering in Medicine and Biology Society, (2008)4628–4631. 
[9] Alwan, M., Rajendran, P., Kell, S., Mack, D., Dalal, S., Wolfe,M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: IEEE Proceedings of International Conference on Information and Communication Technologies: From Theory to Applications, (2006)1003–1007.
[10] Anderson, D., Keller, J.M., Skubic, M., Chen, X., He, Z.: Recognizing falls from silhouettes. In: Annual Int. Conf. of the Engineering in Medicine and Biology Society, pp. 6388–6391 (2006).
[11] Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3D head tracking to detect falls of elderly people. In: Annual Int. Conf. of the IEEE Eng. in Medicine and Biology Society, (2006) 6384–6387.
[12] Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Expert Syst. 24(5), (2007)334–345.
[13] Miaou, S.-G., Sung, P.-H., Huang, C.-Y.: A customized human fall detection system using omni-camera images and personal information. Distributed Diagnosis and Home Healthcare, (2006)39–42.
[14] Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Proc. IEEE Pervasive Health Conference and Workshops, (2006) 1–4.
[15] Kepski, M., Kwolek, B., Austvoll, I.: Fuzzy inference-based reliable fall detection using Kinect and accelerometer. In: The 11th Int. Conf. on Artificial Intelligence and Soft Computing. LNCS, vol. 7267, Springer, (2012)266–273.
[16] Kepski, M., Kwolek, B.: Fall detection on embedded platform using Kinect and wireless accelerometer. In: 13th Int. Conf. on Computers Helping People with Special Needs. LNCS, vol. 7383, Springer, (2012)407–414
[17] Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. of Real-Time Image Processing, (2012)1–12.