Adaptação de um instrumento de rastreio cognitivo, o MoCA (Montreal Cognitive Assessment) para população cabo-verdiana

Dissertação de Mestrado, Neurociências Cognitivas e Neuropsicologia, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2016

Global geriatric evaluation of the Algarve population

The ageing of population challenges communities to adapt and evolve to accommodate the needs of people that live longer (mostly out of work, either healthy, fragile or with chronic disease). Population ageing in the Algarve is higher than in overall Portugal. Studies on health conditions, frailty risk factors and elderly specific needs are undeveloped in Portugal and unknown in the Algarve. Objective To prepare a tool for Global Geriatric Evaluation, to be used in the “Survey of Health and Ageing in the Region of Algarve - SHARA”, a commitment to “European Innovation Partnership on Active and Healthy Ageing”. Methods A preliminary version of the screening tool, which includes well-known instruments to measure health condition (EASY-care), risk of fall (Tinetty), physical activity (Baecke’s modified questionnaire), nutritional condition (MNA), cognitive and depressive status (MMSE, Yesavage geriatric depression scale), together with socio-demographic characteristics, was applied to an independent sample of subjects from an elderly community centre - ARPI (“Associação de Reformados, Pensionistas e Idosos do Concelho de Faro”), with ages between 55 and 89. Results ARPI is mostly frequented by women, who either have risk of malnutrition or malnutrition incidence, a relevant risk of fall or are physically active. Those who live alone, show a higher risk of fall. Conclusions ARPI members are active, but with risk of malnutrition and fall, suggesting the relevance and importance of future interventions in these areas. The proposed screening tool showed to be adequate for the SHARA study, suitable to provide wider information on frailty.

BIMP: A real-time biological model of multi-scale keypoint detection in V1

We present an improved, biologically inspired and multiscale keypoint operator. Models of single- and double-stopped hypercomplex cells in area V1 of the mammalian visual cortex are used to detect stable points of high complexity at multiple scales. Keypoints represent line and edge crossings, junctions and terminations at fine scales, and blobs at coarse scales. They are detected by applying first and second derivatives to responses of complex cells in combination with two inhibition schemes to suppress responses along lines and edges. A number of optimisations make our new algorithm much faster than previous biologically inspired models, achieving real-time performance on modern GPUs and competitive speeds on CPUs. In this paper we show that the keypoints exhibit state-of-the-art repeatability in standardised benchmarks, often yielding best-in-class performance. This makes them interesting both in biological models and as a useful detector in practice. We also show that keypoints can be used as a data selection step, significantly reducing the complexity in state-of-the-art object categorisation. (C) 2014 Elsevier B.V. All rights reserved.