Intelligent detection of the falls in the elderly using fuzzy inference system and video-based motion estimation method

Automatic detection of the abnormal walking in people, especially such accidents as the falls in the elderly, based on image processing techniques and computer vision can help develop an efficient system that its implementation in various contexts enables us to monitor people's movements. This paper proposes a new algorithm, which drawing on fuzzy rules in classification of movements as well as the implementation of the motion estimation, allows the rapid processing of the input data. At the testing stage, 57425 video frames received from Mother Nursing Home in Farzanegan and the video sequences containing the falls of the elderly were used. The results show that the values of average accuracy (AAC), detection rate (DR) and false alarm rate (FAR) were at an acceptable level, respectively with 93%, 89% and 5%. Compared to the similar techniques, the implementation of the proposed system in nursing homes and residential areas allow the real time and intelligent monitoring of the people.

[1]  Wann-Yun Shieh,et al.  Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system. , 2012, Medical engineering & physics.

[2]  James M. Keller,et al.  Technology for Successful Aging , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  N. Vodjdani,et al.  The ambient assisted living joint programme , 2008, 2008 2nd Electronics System-Integration Technology Conference.

[4]  J. Deitz,et al.  Interrater and test-retest reliability of two pediatric balance tests. , 1990, Physical therapy.

[5]  Marjorie Skubic,et al.  Sensor systems for monitoring functional status in assisted living facility residents. , 2008, Research in gerontological nursing.

[6]  Chung-Lin Huang,et al.  Slip and fall event detection using Bayesian Belief Network , 2012, Pattern Recognit..

[7]  Chittaranjan A. Mandal,et al.  Automatic Detection of Human Fall in Video , 2007, PReMI.

[8]  M N Nyan,et al.  A wearable system for pre-impact fall detection. , 2008, Journal of biomechanics.

[9]  Tong Zhang,et al.  Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm , 2006 .

[10]  C. Kisner,et al.  Static and dynamic balance responses in persons with bilateral knee osteoarthritis. , 1997, The Journal of orthopaedic and sports physical therapy.

[11]  Jorge S. Marques,et al.  Performance evaluation of object detection algorithms for video surveillance , 2006, IEEE Transactions on Multimedia.

[12]  Jean Meunier,et al.  Fall Detection from Human Shape and Motion History Using Video Surveillance , 2007, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07).

[13]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[14]  A. Bourke,et al.  A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. , 2008, Medical engineering & physics.

[15]  Yap-Peng Tan,et al.  Fall Incidents Detection for Intelligent Video Surveillance , 2005, 2005 5th International Conference on Information Communications & Signal Processing.