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Open AccessArticle

Virtual Sensors for Optimal Integration of Human Activity Data

Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico
Facultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tablaje Cat. 13615, Colonia Chuburná Hidalgo Inn, Mérida, Yucatán 97110, Mexico
Fandazione Bruno Kessler Foundation, 38123 Trento, Italy
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas—Sede Mérida, Unidad Académica de Ciencias y Tecnología de la UNAM en Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal, Yucatán 97302, Mexico
Tecnologico de Monterrey, School of Engineering and Sciences, Carretera al Lago de Guadalupe Km. 3.5, Atizapán de Zaragoza, Estado de México 52926, Mexico
Authors to whom correspondence should be addressed.
Sensors 2019, 19(9), 2017;
Received: 20 February 2019 / Revised: 3 April 2019 / Accepted: 4 April 2019 / Published: 29 April 2019
(This article belongs to the Special Issue Computational Intelligence-Based Sensors)
Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality. View Full-Text
Keywords: optimal data integration; virtual sensors; fusion methods optimal data integration; virtual sensors; fusion methods
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MDPI and ACS Style

Aguileta, A.A.; Brena, R.F.; Mayora, O.; Molino-Minero-Re, E.; Trejo, L.A. Virtual Sensors for Optimal Integration of Human Activity Data. Sensors 2019, 19, 2017.

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