{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:15:10Z","timestamp":1781622910334,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Production and Research Cooperation Foundation of Fujian Higher Education Institutions","award":["No.2019H6000"],"award-info":[{"award-number":["No.2019H6000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8\u00d78 pixels to collect the infrared signals, which can ensure users\u2019 privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.<\/jats:p>","DOI":"10.3390\/s21103551","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T06:13:45Z","timestamp":1621491225000},"page":"3551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Cunyi","family":"Yin","sequence":"first","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8305-9291","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiren","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6902-9245","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deying","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59192","DOI":"10.1109\/ACCESS.2018.2873502","article-title":"Sensor-based datasets for Human Activity Recognition\u2014A Systematic Review of Literature","volume":"6","author":"Quero","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1109\/TBME.2014.2309951","article-title":"Unobtrusive Sensing and Wearable Devices for Health Informatics","volume":"61","author":"Zheng","year":"2014","journal-title":"IEEE Trans. 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