{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:50:17Z","timestamp":1740149417477,"version":"3.37.3"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"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":"Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly\u2019s daily life and to help people suffering from cognitive disorders, Parkinson\u2019s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.<\/jats:p>","DOI":"10.3390\/s20195707","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T14:28:02Z","timestamp":1602080882000},"page":"5707","source":"Crossref","is-referenced-by-count":60,"title":["A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-6344","authenticated-orcid":false,"given":"Saedeh","family":"Abbaspour","sequence":"first","affiliation":[{"name":"School of Innovation, Design, and Engineering, M\u00e4lardalen University, 72220 V\u00e4ster\u00e5s, Sweden"},{"name":"Engineering Department, University of Qom, Qom 3716146611, Iran"}]},{"given":"Faranak","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"Engineering Department, University of Qom, Qom 3716146611, Iran"}]},{"given":"Ali","family":"Sedaghatbaf","sequence":"additional","affiliation":[{"name":"RISE Research Institutes of Sweden, 72212 V\u00e4ster\u00e5s, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0784","authenticated-orcid":false,"given":"Hossein","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"School of Innovation, Design, and Engineering, M\u00e4lardalen University, 72220 V\u00e4ster\u00e5s, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7755-4795","authenticated-orcid":false,"given":"Maryam","family":"Vahabi","sequence":"additional","affiliation":[{"name":"School of Innovation, Design, and Engineering, M\u00e4lardalen University, 72220 V\u00e4ster\u00e5s, Sweden"},{"name":"ABB Corporate Research, 72226 V\u00e4ster\u00e5s, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1940-1747","authenticated-orcid":false,"given":"Maria","family":"Linden","sequence":"additional","affiliation":[{"name":"School of Innovation, Design, and Engineering, M\u00e4lardalen University, 72220 V\u00e4ster\u00e5s, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dobrucal\u0131, O., and Barshan, B. 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