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Detection of human impacts by an adaptive energy-based anisotropic algorithm

 

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Opened Access Detection of human impacts by an adaptive energy-based anisotropic algorithm
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Author: Prado-Velasco, Manuel
Ortiz Marín, Rafael
Río Cidoncha, María Gloria del
Department: Universidad de Sevilla. Departamento de Ingeniería Gráfica
Date: 2013-10-10
Published in: International Journal of Environmental Research and Public Health, 10 (10), 4767-4789.
Document type: Article
Abstract: Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results.
Cite: Prado-Velasco, M., Ortiz Marín, R. y Rio Cidoncha, G.d. (2013). Detection of human impacts by an adaptive energy-based anisotropic algorithm. International Journal of Environmental Research and Public Health, 10 (10), 4767-4789.
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URI: http://hdl.handle.net/11441/51393

DOI: 10.3390/ijerph10104767

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This work is under a Creative Commons License: 
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