{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:08:17Z","timestamp":1769555297383,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014national funds","doi-asserted-by":"publisher","award":["UMINHO-VC\/BII\/2021\/03"],"award-info":[{"award-number":["UMINHO-VC\/BII\/2021\/03"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014national funds","doi-asserted-by":"publisher","award":["PD\/BD\/141515\/2018"],"award-info":[{"award-number":["PD\/BD\/141515\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014national funds","doi-asserted-by":"publisher","award":["UIDB\/04436\/2020"],"award-info":[{"award-number":["UIDB\/04436\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014national funds","doi-asserted-by":"publisher","award":["UIDP\/04436\/2020"],"award-info":[{"award-number":["UIDP\/04436\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"national support to R&amp;D units grant","award":["UMINHO-VC\/BII\/2021\/03"],"award-info":[{"award-number":["UMINHO-VC\/BII\/2021\/03"]}]},{"name":"national support to R&amp;D units grant","award":["PD\/BD\/141515\/2018"],"award-info":[{"award-number":["PD\/BD\/141515\/2018"]}]},{"name":"national support to R&amp;D units grant","award":["UIDB\/04436\/2020"],"award-info":[{"award-number":["UIDB\/04436\/2020"]}]},{"name":"national support to R&amp;D units grant","award":["UIDP\/04436\/2020"],"award-info":[{"award-number":["UIDP\/04436\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset\u2019s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.<\/jats:p>","DOI":"10.3390\/s22114028","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Inertial Data-Based AI Approaches for ADL and Fall Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1005-9933","authenticated-orcid":false,"given":"Lu\u00eds M.","family":"Martins","sequence":"first","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4177-2587","authenticated-orcid":false,"given":"Nuno Ferrete","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-058 Guimar\u00e3es, Portugal"},{"name":"MIT Portugal Program, School of Engineering, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Filipa","family":"Soares","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-7203","authenticated-orcid":false,"given":"Cristina P.","family":"Santos","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimar\u00e3es, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window Size Impact in Human Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.bbe.2020.04.007","article-title":"Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks","volume":"40","author":"Altuve","year":"2020","journal-title":"Biocybern. 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