title: Detection of human impacts by an adaptive energy-based anisotropic algorithm.
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.
authors.affiliation: Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain. mpradovelasco@ieee.org.
authors.forename: Manuel
authors.initials: M
authors.lastname: Prado-Velasco
authors.forename: Rafael Ortiz
authors.initials: RO
authors.lastname: Marín
authors.forename: Gloria
authors.initials: G
authors.lastname: del Rio Cidoncha
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D000058:Accidental Falls / Q000517:prevention & control*; D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009043:Motor Activity; D016138:Walking; D055815:Young Adult