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

International Journal of Environmental Research and Public Health

Manuel Prado-Velasco¹  Rafael Ortiz Marín² 

[1] Multilevel Modeling and Emerging Technologies in Bioengineering (M2TB), University of Seville, Escuela Superior de Ingenieros, C. de los Descubrimientos s/n, Sevilla 41092, Spain;

关键词: adaptive algorithm;    energy-based impact detection;    unsupervised learning technique;    telehealth services;    distributed signal processing;    smart sensor;   

DOI  :  10.3390/ijerph10104767

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.