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Automated Monitoring System for Fall Detection in the Elderly
Shadi Khawandi, Bassam Daya, Pierre Chauvet
Pages - 476 - 483     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
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KEYWORDS
Fall detection, Monitoring system, Face detection
ABSTRACT
Falls are a major problem for the elderly people living independently. According to the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event), and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall.
CITED BY (9)  
1 Palumbo, F., Gallicchio, C., Pucci, R., & Micheli, A. (2016). Human activity recognition using multisensor data fusion based on Reservoir Computing. Journal of Ambient Intelligence and Smart Environments, 8(2), 87-107.
2 Mishra, N., & Gaur, R. (2014). Fall Detection And Activity Monitoring For Oldsters Using MEMS Technology. International Journal Of Scientific Research And Education, 2(12).
3 Piva, L. S., Braga, R. B., Ferreira, A. B., & de Castro Andrade, R. M. fAlert: Um sistema android para monitoramento de quedas em pessoas com cuidados especiais.
4 Gjoreski, H., Gams, M., & Luštrek, M. (2014). Context-based fall detection and activity recognition using inertial and location sensors. Journal of Ambient Intelligence and Smart Environments (JAISE), 6(4), 419-433.
5 El-Bendary, N., Tan, Q., Pivot, F. C., & Lam, A. (2013). Fall detection and prevention for the elderly: A review of trends and challenges. International Journal on Smart Sensing and Intelligent Systems, 6(3), 1230-1266.
6 Palumbo, F., Barsocchi, P., Gallicchio, C., Chessa, S., & Micheli, A. (2013). Multisensor data fusion for activity recognition based on reservoir computing. In Evaluating AAL systems through competitive benchmarking (pp. 24-35). Springer Berlin Heidelberg.
7 Gjoreski, H., Luštrek, M., & Gams, M. (2012). Context-based fall detection using inertial and location sensors. In Ambient Intelligence (pp. 1-16). Springer Berlin Heidelberg.
8 Gjoreski, H., Luštrek, M., & Gams, M. (2011, July). Accelerometer placement for posture recognition and fall detection. In Intelligent Environments (IE), 2011 7th International Conference on (pp. 47-54). IEEE.
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Dr. Shadi Khawandi
- France
skhawandi@hotmail.com
Bassam Daya
-
Pierre Chauvet
-