Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

General information

Authors:
António PEREIRA, Filipe FELISBERTO, Luis MADURO, Miguel FELGUEIRAS

DOI:
10.14201/ADCAIJ2012116277

Volume:
Regular Issue 1 (1), 2012

Keywords:

Ambient assisted living; Elderly; Fall Detection; Logistic Regression; Wireless Sensor Networks

In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.

References

BELSLEY, D.; KUH, E. & WELSCH, R. Regression diagnostics: Identifying influential data and sources of collinearity Wiley-Interscience, 2004

BOURKE, A. & LYONS, G. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm Gait and Posture, Oxford, UK: Butterworth-Heinemann, c1993-, 2007, 26, 194-199

CARONE, G. & de las Comunidades Europeas. Dirección General de Asuntos Económicos y Financieros, C. Long-term labour force projections for the 25 EU Member States: A set of data for assessing the economic impact of ageing European Commission, Directorate-General for Economic and Financial Affairs, 2005

FREEMAN, C.; TODD, C.; CAMILLERI-FERRANTE, C.; LAXTON, C.; MURRELL, P.; PALMER, C.; PARKER, M.; PAYNE, B. & RUSHTON, N. Quality improvement for patients with hip fracture: experience from a multi-site audit Quality and Safety in Health Care, BMJ Publishing Group Ltd, 2002, 11, 239

HOSMER, D. & LEMESHOW, S. Applied logistic regression Wiley-Interscience, 2000

KINSELLA, K.; HE, W. & BUREAU, U. C. An aging world: 2008: International population reports
US Government Printing Office, 2009

MCCULLAGH, P. & NELDER, J. Generalized linear models Chapman & Hall/CRC, 1989

PFLIMLIN, J.; HAMEL, T.; SOUERES, P. & METNI, N. Nonlinear attitude and gyroscope's bias estimation for a VTOL UAV International journal of systems science, Taylor & Francis Ltd, 4 Park Square, Milton Park, Abingdon, OX 14 4 RN, UK,2007, 38, 197-210

STEG, H., et al.: Europe Is Facing a Demographic Challenge - Ambient Assisted Living Offers Solutions. In: VDI/VDE/IT, 2006, Germany

TURKMAN, M. Antónia Amaral; SILVA, Giovani Loiola Modelos Lineares Generalizados. Lisboa: S.P.E., 2000.

WU, G. Distinguishing fall activities from normal activities by velocity characteristics Journal of Biomechanics, 2000, 33, 1497-1500

How to cite item?