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

Manuel Prado-Velasco, Rafael Ortiz Marín and Gloria Del Rio Cidoncha
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Manuel Prado-Velasco: Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain
Rafael Ortiz Marín: Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain
Gloria Del Rio Cidoncha: Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain

IJERPH, 2013, vol. 10, issue 10, 1-23

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.

Keywords: adaptive algorithm; energy-based impact detection; unsupervised learning technique; telehealth services; distributed signal processing; smart sensor (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2013
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