{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T01:25:06Z","timestamp":1766625906366,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,31]],"date-time":"2019-03-31T00:00:00Z","timestamp":1553990400000},"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>In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.<\/jats:p>","DOI":"10.3390\/s19071556","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T03:21:26Z","timestamp":1554175286000},"page":"1556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2323-9335","authenticated-orcid":false,"given":"Carlos","family":"Avil\u00e9s-Cruz","sequence":"first","affiliation":[{"name":"Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico"}]},{"given":"Andr\u00e9s","family":"Ferreyra-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico"}]},{"given":"Arturo","family":"Z\u00fa\u00f1iga-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8918-1044","authenticated-orcid":false,"given":"Juan","family":"Villegas-Cort\u00e9z","sequence":"additional","affiliation":[{"name":"Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. 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