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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>\n            With the emerging interest in the ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual\u2019s behavioral pattern (e.g., physical activity, location, and mood) by using sensors embedded in smart-watches and smartphones. The multi-sensor environments also come with some challenges, such as how to fuse and combine different sources of data. In this article, we explore several methods of fusion for multi-representations of data from sensors. Furthermore, multiple representations of sensor data were generated and then fused using\n            <jats:italic>data-level, feature-level<\/jats:italic>\n            , and\n            <jats:italic>decision-level fusions<\/jats:italic>\n            . The presented methods were evaluated using three publicly available human activity recognition (HAR) datasets. The presented approaches utilize Deep Convolutional Neural Networks (CNNs). A generic architecture for fusion of different sensors is proposed. The proposed method shows promising performance, with the best results reaching an overall accuracy of 98.4% for the Context-Awareness via Wrist-Worn Motion Sensors (HANDY) dataset and 98.7% for the Wireless Sensor Data Mining (WISDM version 1.1) dataset. Both results outperform previous approaches.\n          <\/jats:p>","DOI":"10.1145\/3377882","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T22:07:21Z","timestamp":1590444441000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2256-3835","authenticated-orcid":false,"given":"Farzan Majeed","family":"Noori","sequence":"first","affiliation":[{"name":"Department of Informatics, University of Oslo, Oslo, Norway"}]},{"given":"Michael","family":"Riegler","sequence":"additional","affiliation":[{"name":"Simula Metropolitan Center for Digitalization and Kristiania University College, Oslo, Norway"}]},{"given":"Md Zia","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo, Oslo, Norway"}]},{"given":"Jim","family":"Torresen","sequence":"additional","affiliation":[{"name":"Department of Informatics and RITMO, University of Oslo, Oslo, Norway"}]}],"member":"320","published-online":{"date-parts":[[2020,5,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/data3030024"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISM.2018.000-2"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.01.023"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s120608039"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499621"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.05.007"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.01.025"},{"key":"e_1_2_1_8_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. 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