Embedded system used for classifying motor activities of elderly and disabled people

Our modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people to live longer in their own home, ensuring the necessary vigilance and security at the lowest cost, some tele-assistance systems are already available commercially. This paper presents an embedded prototype able to detect automatically the falls of elderly people while monitoring their motor activities. The classification algorithm using an artificial neural network, the communication and location capabilities of this system are specifically highlighted. In the last part, some experimental results and social issues stemming from Gerontologic Institute Ingema are discussed.

[1]  E. Simões,et al.  Dependence in Activities of Daily Living and Cognitive Impairment Strongly Predicted Mortality in Older Urban Residents in Brazil: A 2‐Year Follow‐Up , 2001, Journal of the American Geriatrics Society.

[2]  S H Holzreiter,et al.  Assessment of gait patterns using neural networks. , 1993, Journal of biomechanics.

[3]  G. Fuller,et al.  Falls in the elderly. , 2000, American family physician.

[4]  Suzanne Martin,et al.  Using Commercially Available Technology to Assist in the Delivery of Person-Centred Health and Social Care , 2002, Journal of telemedicine and telecare.

[5]  M. Borrie,et al.  Circumstances and consequences of falls experienced by a community population 70 years and over during a prospective study. , 1990, Age and ageing.

[6]  L. Fried,et al.  Frailty in older adults: evidence for a phenotype. , 2001, The journals of gerontology. Series A, Biological sciences and medical sciences.

[7]  Jyrki Lötjönen,et al.  Automatic sleep-wake and nap analysis with a new wrist worn online activity monitoring device vivago WristCare. , 2003, Sleep.

[8]  M Runge [Multifactorial pathogenesis of gait disorders, falls and hip fractures in the elderly]. , 1997, Zeitschrift fur Gerontologie und Geriatrie.

[9]  W. Hazzard,et al.  Principles of Geriatric Medicine and Gerontology , 2003 .

[10]  Y. Lajoie,et al.  Step Length Variability at Gait Initiation in Elderly Fallers and Non-Fallers, and Young Adults , 2002, Gerontology.

[11]  Jacques Demongeot,et al.  A system for automatic measurement of circadian activity deviations in telemedicine , 2002, IEEE Transactions on Biomedical Engineering.

[12]  Phiroz Bhagat,et al.  Pattern Recognition in Industry , 2006 .

[13]  R. Cumming,et al.  Fall Frequency and Characteristics and the Risk of Hip Fractures , 1994, Journal of the American Geriatrics Society.

[14]  M. Hawley,et al.  Automatic fall detectors and the fear of falling , 2004, Journal of telemedicine and telecare.

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  S. Õunpuu,et al.  Effects of age on the biomechanics and physiology of gait. , 1996, Clinics in geriatric medicine.

[17]  Theodore R. Reiff,et al.  Clinical Geriatric Medicine , 1983 .

[18]  P O Riley,et al.  Effect of age on lower extremity joint moment contributions to gait speed. , 2001, Gait & posture.

[19]  A. Clarke,et al.  Respite care for frail older people and their family carers: concept analysis and user focus group findings of a pan-European nursing research project. , 1999, Journal of advanced nursing.