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Int. J. Environ. Res. Public Health 2013, 10(10), 4767-4789; doi:10.3390/ijerph10104767

Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm

Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain
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Received: 10 July 2013 / Revised: 22 September 2013 / Accepted: 22 September 2013 / Published: 10 October 2013
(This article belongs to the Special Issue Advances in Telehealthcare)
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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. View Full-Text
Keywords: adaptive algorithm; energy-based impact detection; unsupervised learning technique; telehealth services; distributed signal processing; smart sensor adaptive algorithm; energy-based impact detection; unsupervised learning technique; telehealth services; distributed signal processing; smart sensor
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Prado-Velasco, M.; Marín, R.O.; del Rio Cidoncha, G. Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm. Int. J. Environ. Res. Public Health 2013, 10, 4767-4789.

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