{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T14:33:57Z","timestamp":1779114837052,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC 111-2221-E-324-003-MY3"],"award-info":[{"award-number":["NSTC 111-2221-E-324-003-MY3"]}]},{"name":"National Science and Technology Council","award":["NSTC 111-2221-E-027-134"],"award-info":[{"award-number":["NSTC 111-2221-E-027-134"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people\u2019s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.<\/jats:p>","DOI":"10.3390\/s23063354","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T08:36:16Z","timestamp":1679474176000},"page":"3354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch"],"prefix":"10.3390","volume":"23","author":[{"given":"Tan-Hsu","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jyun-Yu","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3923-4387","authenticated-orcid":false,"given":"Shing-Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8933-7483","authenticated-orcid":false,"given":"Mohammad","family":"Alkhaleefah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5834-1057","authenticated-orcid":false,"given":"Yang-Lang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-7435","authenticated-orcid":false,"given":"Munkhjargal","family":"Gochoo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, United Arab Emirates University, Al-Ain 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021, December 01). 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