Viusal fall alert service in low computational power device to assist persons' with dementia

In this paper, we propose a computationally efficient fall detection algorithm which exploits visual observations. The proposed architecture is designed to be suitable for devices of low processing capabilities allowing a large scale implementation of the proposed IT technology in the area of aiding elderly or persons' with dementia. In contrast to other approaches, visual surveillance is more natural way for detecting persons' falls, especially for people of dementia where falls are often repeatedly. The algorithm has been evaluated in the lab and real-life clinical conditions, indicating a robust fall detection even in cases of high environmental changes.

[1]  Nikolaos F. Matsatsinis,et al.  Self Adaptive background modeling for identifying persons' falls , 2010, 2010 Fifth International Workshop Semantic Media Adaptation and Personalization.

[2]  Nikolaos D. Doulamis,et al.  Iterative motion estimation constrained by time and shape for detecting persons' falls , 2010, PETRA '10.

[3]  J. Naslund.  Visuospatial ability in relation to fall risk and dementia. , 2010, Archives of neurology.

[4]  C. Stentoumis,et al.  A REAL-TIME SINGLE-CAMERA APPROACH FOR AUTOMATIC FALL DETECTION , 2010 .

[5]  Thinh M. Le,et al.  Accelerometer-based sensor network for fall detection , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[6]  Mariko Yamamoto,et al.  Severity of dementia as a risk factor for repeat falls among the institutionalized elderly in Japan. , 2009, Nursing & health sciences.

[7]  C. Ballard,et al.  Incidence and Prediction of Falls in Dementia: A Prospective Study in Older People , 2009, PloS one.

[8]  Alireza Rezvanian,et al.  Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

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

[10]  Nicolas Thome,et al.  A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Eugenio Culurciello,et al.  An Address-Event Fall Detector for Assisted Living Applications , 2008, IEEE Transactions on Biomedical Circuits and Systems.

[12]  Shuangquan Wang,et al.  Human activity recognition with user-free accelerometers in the sensor networks , 2005, 2005 International Conference on Neural Networks and Brain.

[13]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[14]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.