{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:34:40Z","timestamp":1773246880938,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human\u2013Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users\u2019 smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.<\/jats:p>","DOI":"10.3390\/info14070404","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:40:06Z","timestamp":1689324006000},"page":"404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques"],"prefix":"10.3390","volume":"14","author":[{"given":"Hossein","family":"Shahverdi","sequence":"first","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 19839 69411, Iran"}]},{"given":"Mohammad","family":"Nabati","sequence":"additional","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 19839 69411, Iran"}]},{"given":"Parisa","family":"Fard Moshiri","sequence":"additional","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 19839 69411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9898-7744","authenticated-orcid":false,"given":"Reza","family":"Asvadi","sequence":"additional","affiliation":[{"name":"Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 19839 69411, 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 E16 2RD, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hassan, Q.F. (2018). Internet of Things A to Z: Technologies and Applications, Wiley-IEEE Press. [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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