{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:28:45Z","timestamp":1776083325120,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Smart Sustainable New Agriculture Research Center (SMARTer) at the NSTC of Taiwan","award":["111-2634-F-005-001"],"award-info":[{"award-number":["111-2634-F-005-001"]}]},{"name":"Innovation and Development Center of Sustainable Agriculture","award":["111-2634-F-005-001"],"award-info":[{"award-number":["111-2634-F-005-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.<\/jats:p>","DOI":"10.3390\/s23187802","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T10:42:49Z","timestamp":1694428969000},"page":"7802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction"],"prefix":"10.3390","volume":"23","author":[{"given":"Yu-Hsuan","family":"Tseng","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6007-9361","authenticated-orcid":false,"given":"Chih-Yu","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan"},{"name":"Smart Sustainable New Agriculture Research Center (SMARTer), National Chung Hsing University, Taichung 40227, Taiwan"},{"name":"Innovation and Development Center of Sustainable Agriculture (IDCSA), National Chung Hsing University, Taichung 40227, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neucom.2021.11.044","article-title":"DCNN based human activity recognition framework with depth vision guiding","volume":"486","author":"Qi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8553","DOI":"10.1109\/JIOT.2019.2920283","article-title":"IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment","volume":"6","author":"Bianchi","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, Y.W., Joa, K.L., Jeong, H.Y., and Lee, S. 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