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Financed by Ministerio de Economía y competitividad. TIN2013-47152-C3-1-R

From September 2014 to September 2017

Frailty is a clinical syndrome related with age where the early detection and diagnosis constitutes a key indicator to improve the elderly people’s quality of life. However, offering a frailty valuation is still a complex task, due to the combination of all aspects that have to be taken into account to give place to an integral and valuable diagnosis. Likewise, physical frailty is associated with a major risk of cognitive decline. The presence of a pathological gait is strongly linked to the presence of dementia in general.

It is proven that gait disorders are a prevalent problem in the elderly. The same occurs with the memory and dementia issues. The quantitative evaluation of gait could avoid the underlying subjectivity in the geriatrician diagnosis of frailty and dementia usually based on gait anomalies’ observation. An objective and precise analysis of the spatial and temporal features of gait, through the suitable devices, can provide useful information to undertake the early diagnosis of dementia and to better understand the risk of falls in subjects with cognitive deterioration.

The aim of this coordinated project is to provide a minimally invasive, low-cost and easily deployable solution to enable, through accelerometry, vision and gait analysis by means of evolution and customizable inference mechanisms, to diagnose the frailty and senility syndromes and study their temporal evolution. The solution proposed by FRASE will follow a Mobile Cloud Computing approach combining local processing, in the elderly people’s living environment through their smartphones or other embedded computing devices, and remote processing in the Cloud to undertake more computing costly processes that imply the correlation of history data associated to groups of elderlies.

The project contributes with research about preventive attention techniques to the syndromes of frailty and dementia and in the development of intelligent systems that help in the diagnosis of those syndromes. The development effort is oriented towards accomplishing classification algorithms and specialised inference to characterize those syndromes. The contributions in the areas of sensing and intelligence proposed, and the architectural solution that brings them together, may be extrapolated to other scenarios within the AAL field. For instance, they could be applied to daily activities monitoring at home and the detection of significant variants in the way those activities are undertaken by the elderly people. In essence, FRASE contributes significantly towards a universalization of Ambient Assisted Living systems