{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:41:20Z","timestamp":1783186880667,"version":"3.54.6"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Slovak Research and Development Agency","award":["APVV-21-0502"],"award-info":[{"award-number":["APVV-21-0502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, three-dimensional convolutional neural networks (3DCNNs) are a popular approach in the field of human activity recognition. However, due to the variety of methods used for human activity recognition, we propose a new deep-learning model in this paper. The main objective of our work is to optimize the traditional 3DCNN and propose a new model that combines 3DCNN with Convolutional Long Short-Term Memory (ConvLSTM) layers. Our experimental results, which were obtained using the LoDVP Abnormal Activities dataset, UCF50 dataset, and MOD20 dataset, demonstrate the superiority of the 3DCNN + ConvLSTM combination for recognizing human activities. Furthermore, our proposed model is well-suited for real-time human activity recognition applications and can be further enhanced by incorporating additional sensor data. To provide a comprehensive comparison of our proposed 3DCNN + ConvLSTM architecture, we compared our experimental results on these datasets. We achieved a precision of 89.12% when using the LoDVP Abnormal Activities dataset. Meanwhile, the precision we obtained using the modified UCF50 dataset (UCF50mini) and MOD20 dataset was 83.89% and 87.76%, respectively. Overall, our work demonstrates that the combination of 3DCNN and ConvLSTM layers can improve the accuracy of human activity recognition tasks, and our proposed model shows promise for real-time applications.<\/jats:p>","DOI":"10.3390\/s23052816","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T02:28:34Z","timestamp":1678069714000},"page":"2816","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["A New Deep-Learning Method for Human Activity Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4766-8940","authenticated-orcid":false,"given":"Roberta","family":"Vrskova","sequence":"first","affiliation":[{"name":"Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4875-973X","authenticated-orcid":false,"given":"Patrik","family":"Kamencay","sequence":"additional","affiliation":[{"name":"Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7543-5664","authenticated-orcid":false,"given":"Robert","family":"Hudec","sequence":"additional","affiliation":[{"name":"Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Sykora","sequence":"additional","affiliation":[{"name":"Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e5302","DOI":"10.1002\/cpe.5302","article-title":"An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia data analysis","volume":"33","author":"Wang","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/978-3-031-04409-0_9","article-title":"3DCNN Backed Conv-LSTM Auto Encoder for Micro Facial Expression Video Recognition","volume":"438","author":"Islam","year":"2021","journal-title":"Mach. Learn. Intell. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/TMM.2018.2869278","article-title":"Continuous Gesture Segmentation and Recognition using 3DCNN and Convolutional LSTM","volume":"21","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117451","DOI":"10.1016\/j.eswa.2022.117451","article-title":"GssMILP for anomaly classification in surveillance videos","volume":"203","author":"Krishna","year":"2022","journal-title":"IEEE Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pediaditis, M., Farmaki, C., Schiza, S., Tzanakis, N., Galanakis, E., and Sakkalis, V. (2022, January 21\u201323). Contactless respiratory rate estimation from video in a real-life clinical environment using eulerian magnification and 3D CNNs. Proceedings of the IEEE International Conference on Imaging Systems and Techniques, Kaohsiung, Taiwan.","DOI":"10.1109\/IST55454.2022.9827675"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.neucom.2021.03.004","article-title":"Vision-assisted recognition of stereotype behaviors for early diagnosis of Autism Spectrum Disorders","volume":"446","author":"Negin","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ka\u00e7dioglu, S., \u00d6zyer, B., and \u00d6zyer, G.T. (2020, January 5\u20137). Recognizing Self-Stimulatory Behaviours for Autism Spectrum Disorders. Proceedings of the Signal Processing and Communications Applications Conference, Gaziantep, Turkey.","DOI":"10.1109\/SIU49456.2020.9302403"},{"key":"ref_8","first-page":"77","article-title":"Recognition of Farmers\u2019 Working Based on HC-LSTM Model","volume":"813","author":"Zhao","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhu, G., Shen, P., Song, J., Shah, S.A., and Bennamoun, M. (2017, January 22\u201329). Learning Spatiotemporal Features Using 3DCNN and Convolutional LSTM for Gesture Recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.369"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9893","DOI":"10.1109\/ACCESS.2018.2890675","article-title":"InnoHAR: A Deep Neural Network for Complex Human Activity Recognition","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Almabdy, S., and Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Appl. Sci., 9.","DOI":"10.3390\/app9204397"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108153","DOI":"10.1016\/j.patcog.2021.108153","article-title":"Knowledge Base Graph Embedding Module Design for Visual Question Answering Model","volume":"120","author":"Zheng","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mutegeki, R., and Han, D.S. (2020, January 19\u201321). A CNN-LSTM Approach to Human Activity Recognition. Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan.","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vrskova, R., Hudec, R., Kamencay, P., and Sykora, P. (2022). A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture. Sensors, 22.","DOI":"10.3390\/s22082946"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vrskova, R., Hudec, R., Kamencay, P., and Sykora, P. (2022). Human Activity Classification Using the 3DCNN Architecture. Appl. Sci., 12.","DOI":"10.3390\/app12020931"},{"key":"ref_16","first-page":"221","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Chengping","year":"2013","journal-title":"Comput. Mater. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/MCOM.001.1900396","article-title":"Deep learning serves voice cloning: How vulnerable are automatic speaker verification systems to spooting trial","volume":"58","author":"Partila","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization","volume":"184","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Zhou, X., and Yang, T. (2018, January 19\u201323). Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219922"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s00138-012-0450-4","article-title":"Recognizing 50 Human Action Categories of Web Videos","volume":"24","author":"Reddy","year":"2013","journal-title":"Mach. Vis. Appl. J. (MVAP)"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/THMS.2020.2971958","article-title":"A Multiviewpoint Outdoor Dataset for Human Action Recognition","volume":"50","author":"Perera","year":"2020","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ghodhbani, E., Kaanich, M., and Benazza-Benyahia, A. (2021, January 8\u201310). An Effective 3D ResNet Architecture for Stereo Image Retrieval. Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021), Virtual Event.","DOI":"10.5220\/0010261103800387"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2816\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:48Z","timestamp":1760122068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052816"],"URL":"https:\/\/doi.org\/10.3390\/s23052816","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,4]]}}}