{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:39:14Z","timestamp":1777127954251,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"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>The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users\u2019 inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.<\/jats:p>","DOI":"10.3390\/s21217225","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":121,"title":["A CSI-Based Human Activity Recognition Using Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Parisa","family":"Fard Moshiri","sequence":"first","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 1983969411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2313-6002","authenticated-orcid":false,"given":"Reza","family":"Shahbazian","sequence":"additional","affiliation":[{"name":"Electrical Engineering Research Group, Faculty of Technology and Engineering Research Center, Standard Research Institute, Alborz 31745-139, Iran"}]},{"given":"Mohammad","family":"Nabati","sequence":"additional","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 1983969411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2910-9208","authenticated-orcid":false,"given":"Seyed Ali","family":"Ghorashi","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London E15 4LZ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hassan, Q.F. (2018). Internet of Things A to Z: Technologies and Applications, Wiley. [1st ed.].","DOI":"10.1002\/9781119456735"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dey, N., Hassanien, A.E., Bhatt, C., Ashour, A.S., and Satapathy, S.C. (2018). Internet of Things and Big Data Analytics toward Next-Generation Intelligence, Springer. 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