{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:56:13Z","timestamp":1777380973226,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2023,11,16]]},"abstract":"<jats:p>Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model\u2019s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.<\/jats:p>","DOI":"10.3233\/ais-230174","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:22:47Z","timestamp":1700220167000},"page":"1-14","source":"Crossref","is-referenced-by-count":2,"title":["Wavelet-domain human activity recognition utilizing convolutional neural networks"],"prefix":"10.1177","author":[{"given":"Mohammad","family":"Tavakkoli","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehsan","family":"Nazerfard","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Amirmazlaghani","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"20","key":"10.3233\/AIS-230174_ref1","doi-asserted-by":"publisher","first-page":"13705","DOI":"10.1007\/s00521-021-06007-5","article-title":"Dilated causal convolution with multi-head self attention for sensor human activity recognition","volume":"33","author":"Ali Hamad","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/AIS-230174_ref2","doi-asserted-by":"crossref","unstructured":"S.\u00a0Ali Rokni, M.\u00a0Nourollahi and H.\u00a0Ghasemzadeh, Personalized human activity recognition using convolutional neural networks, in: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018, pp.\u00a08143\u20138144. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/12185. ISBN 9781577358008.","DOI":"10.1609\/aaai.v32i1.12185"},{"key":"10.3233\/AIS-230174_ref3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-319-13105-4_14","article-title":"mHealthDroid: A novel framework for agile development of mobile health applications","volume":"8868","author":"Banos","year":"2014","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"10.3233\/AIS-230174_ref4","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1016\/j.asoc.2015.01.025","article-title":"On the use of ensemble of classifiers for accelerometer-based activity recognition","volume":"37","author":"Catal","year":"2015","journal-title":"Applied Soft Computing Journal"},{"key":"10.3233\/AIS-230174_ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2015.263"},{"key":"10.3233\/AIS-230174_ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2016.7743581"},{"issue":"1","key":"10.3233\/AIS-230174_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3191739","article-title":"TagFree activity identification with RFIDs","volume":"2","author":"Fan","year":"2018","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"10.3233\/AIS-230174_ref9","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/602358"},{"key":"10.3233\/AIS-230174_ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"10.3233\/AIS-230174_ref11","unstructured":"N.Y.\u00a0Hammerla, S.\u00a0Halloran and T.\u00a0Pl\u00f6tz, Deep, convolutional, and recurrent models for human activity recognition using wearables, in: IJCAI International Joint Conference on Artificial Intelligence, 2016-Janua, 2016, pp.\u00a01533\u20131540. http:\/\/arxiv.org\/abs\/1604.08880."},{"issue":"10","key":"10.3233\/AIS-230174_ref12","doi-asserted-by":"publisher","first-page":"25474","DOI":"10.3390\/s151025474","article-title":"Analysis of movement, orientation and rotation-based sensing for phone placement recognition","volume":"15","author":"Incel","year":"2015","journal-title":"Sensors (Switzerland)"},{"key":"10.3233\/AIS-230174_ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806333"},{"key":"10.3233\/AIS-230174_ref15","unstructured":"D.P.\u00a0Kingma and J.L.\u00a0Ba, Adam: A method for stochastic optimization, in: 3rd International Conference on Learning Representations, ICLR 2015 \u2013 Conference Track Proceedings, 2015, https:\/\/arxiv.org\/abs\/1412.6980v9."},{"key":"10.3233\/AIS-230174_ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300568"},{"key":"10.3233\/AIS-230174_ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994569"},{"key":"10.3233\/AIS-230174_ref18","doi-asserted-by":"publisher","DOI":"10.3390\/s22020635"},{"key":"10.3233\/AIS-230174_ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018682"},{"key":"10.3233\/AIS-230174_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"key":"10.3233\/AIS-230174_ref21","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","article-title":"Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges","volume":"105","author":"Nweke","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/AIS-230174_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BSN.2016.7516235"},{"key":"10.3233\/AIS-230174_ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD45719.2019.8942124"},{"key":"10.3233\/AIS-230174_ref24","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.neucom.2020.10.056","article-title":"Cross-subject transfer learning in human activity recognition systems using generative adversarial networks","volume":"426","author":"Soleimani","year":"2021","journal-title":"Neurocomputing"},{"key":"10.3233\/AIS-230174_ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2021.3136642"},{"key":"10.3233\/AIS-230174_ref26","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/AIS-230174_ref27","doi-asserted-by":"publisher","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","article-title":"Smartphone and smartwatch-based biometrics using activities of daily living","volume":"7","author":"Weiss","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/AIS-230174_ref28","doi-asserted-by":"publisher","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","article-title":"LSTM-CNN architecture for human activity recognition","volume":"8","author":"Xia","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/AIS-230174_ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00087"},{"key":"10.3233\/AIS-230174_ref30","doi-asserted-by":"publisher","first-page":"5262","DOI":"10.1109\/ACCESS.2017.2684913","article-title":"Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering","volume":"5","author":"Zdravevski","year":"2017","journal-title":"IEEE Access"},{"key":"10.3233\/AIS-230174_ref31","doi-asserted-by":"publisher","DOI":"10.4108\/icst.mobicase.2014.257786"},{"issue":"2","key":"10.3233\/AIS-230174_ref32","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1109\/JIOT.2019.2949715","article-title":"A novel iot-perceptive human activity recognition (har) approach using multihead convolutional attention","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Internet of Things Journal"},{"key":"10.3233\/AIS-230174_ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370438"},{"key":"10.3233\/AIS-230174_ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106082"},{"issue":"13","key":"10.3233\/AIS-230174_ref35","doi-asserted-by":"publisher","first-page":"7723","DOI":"10.1007\/s00521-020-05514-1","article-title":"Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification","volume":"33","author":"Zheng","year":"2021","journal-title":"Neural Computing and Applications"}],"container-title":["Journal of Ambient Intelligence and Smart Environments"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIS-230174","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:19:31Z","timestamp":1777367971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospress&doi=10.3233\/AIS-230174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,16]]},"references-count":33,"URL":"https:\/\/doi.org\/10.3233\/ais-230174","relation":{},"ISSN":["1876-1372","1876-1364"],"issn-type":[{"value":"1876-1372","type":"electronic"},{"value":"1876-1364","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11,16]]}}}