A Healthy Monitor System for Fall and Balance Detection of Elderly

Z. J. Hou†*, Z. Q. Lu†, J. Z. Liang†, C. Chen‡, & Y. Xu†

†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.



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