Falls and falls efficacy: the role of sustained attention in older adults

<p>Abstract</p> <p>Background</p> <p>Previous evidence indicates that older people allocate more of their attentional resources toward their gait and that the attention-related changes that occur during aging increase the risk of falls. The aim of this study was to investigate whether performance and variability in sustained attention is associated with falls and falls efficacy in older adults.</p> <p>Methods</p> <p>458 community-dwelling adults aged ≥ 60 years underwent a comprehensive geriatric assessment. Mean and variability of reaction time (RT), commission errors and omission errors were recorded during a fixed version of the Sustained Attention to Response Task (SART). RT variability was decomposed using the Fast Fourier Transform (FFT) procedure, to help characterise variability associated with the arousal and vigilance aspects of sustained attention.</p> <p>The number of self-reported falls in the previous twelve months, and falls efficacy (Modified Falls Efficacy Scale) were also recorded.</p> <p>Results</p> <p>Significant increases in the mean and variability of reaction time on the SART were significantly associated with both falls (p < 0.01) and reduced falls efficacy (p < 0.05) in older adults. An increase in omission errors was also associated with falls (p < 0.01) and reduced falls efficacy (p < 0.05). Upon controlling for age and gender affects, logistic regression modelling revealed that increasing variability associated with the vigilance (top-down) aspect of sustained attention was a retrospective predictor of falling (p < 0.01, OR = 1.14, 95% CI: 1.03 - 1.26) in the previous year and was weakly correlated with reduced falls efficacy in non-fallers (p = 0.07).</p> <p>Conclusions</p> <p>Greater variability in sustained attention is strongly correlated with retrospective falls and to a lesser degree with reduced falls efficacy. This cognitive measure may provide a novel and valuable biomarker for falls in older adults, potentially allowing for early detection and the implementation of preventative intervention strategies.</p

to the analysis

Theoretical, statistical, and practical perspective

Face recognition algorithms surpass humans

Abstract—There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be “difficult ” and six algorithms surpassed humans on “easy ” face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control—humans. Index Terms—Face and gesture recognition, performance evaluation of algorithms and systems, human information processing.

La recherche à l'IGN : activités 2003

Bulletin d'information de l'IGN N°75Ce bulletin dresse le bilan des activités menées en 2003 par l'Institut géographique national dans le cadre de sa mission de recherche spécifique dans le domaine de l’information géographique et livre une série d'articles scientifiques rédigés par des chercheurs des laboratoires de l'IGN. Ces articles font écho aux treizièmes journées de la Recherche qui se sont tenues les 10 et 11 mars 2004 dans les locaux de l’École national des sciences géographiques à Marne la Vallé