{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:40:04Z","timestamp":1769017204413,"version":"3.49.0"},"reference-count":70,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method.<\/jats:p>","DOI":"10.3390\/s20082350","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"2350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Choosing the Best Sensor Fusion Method: A Machine-Learning Approach"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0995-2273","authenticated-orcid":false,"given":"Ramon F.","family":"Brena","sequence":"first","affiliation":[{"name":"Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5155-3543","authenticated-orcid":false,"given":"Antonio A.","family":"Aguileta","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico"},{"name":"Facultad de Matem\u00e1ticas, Universidad Aut\u00f3noma de Yucat\u00e1n, Anillo Perif\u00e9rico Norte, Tablaje Cat. 13615, Colonia Chuburn\u00e1 Hidalgo Inn, M\u00e9rida 97110, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9741-4581","authenticated-orcid":false,"given":"Luis A.","family":"Trejo","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, School of Engineering and Sciences, Carretera al Lago de Guadalupe Km. 3.5, Atizap\u00e1n de Zaragoza 52926, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7615-4431","authenticated-orcid":false,"given":"Erik","family":"Molino-Minero-Re","sequence":"additional","affiliation":[{"name":"Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas\u2014Sede M\u00e9rida, Unidad Acad\u00e9mica de Ciencias y Tecnolog\u00eda de la UNAM en Yucat\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Sierra Papacal 97302, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5773-3876","authenticated-orcid":false,"given":"Oscar","family":"Mayora","sequence":"additional","affiliation":[{"name":"Fandazione Bruno Kessler Foundation, 38123 Trento, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.inffus.2016.09.005","article-title":"Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges","volume":"35","author":"Gravina","year":"2017","journal-title":"Inf. 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