{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T15:38:12Z","timestamp":1771688292284,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11760-024-03552-z","type":"journal-article","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:01:41Z","timestamp":1726876901000},"page":"9375-9385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-modal hybrid hierarchical classification approach with transformers to enhance complex human activity recognition"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4961-0171","authenticated-orcid":false,"given":"Mustafa","family":"Ezzeldin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3522-4875","authenticated-orcid":false,"given":"Amr","family":"S. Ghoneim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7928-5680","authenticated-orcid":false,"given":"Laila","family":"Abdelhamid","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4998-5624","authenticated-orcid":false,"given":"Ayman","family":"Atia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"3552_CR1","doi-asserted-by":"publisher","first-page":"53540","DOI":"10.1109\/ACCESS.2021.3070646","volume":"9","author":"Y Shavit","year":"2021","unstructured":"Shavit, Y., Klein, I.: Boosting inertial-based human activity recognition with transformers. IEEE Access 9, 53540\u201353547 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3070646","journal-title":"IEEE Access"},{"key":"3552_CR2","doi-asserted-by":"publisher","unstructured":"Kumar, R., Kumar, S.: Effectiveness of vision transformers in human activity recognition from videos. In: 2023 International Conference on Advancement in Computation and Computer Technologies (InCACCT), pp. 593\u2013597 (2023). https:\/\/doi.org\/10.1109\/InCACCT57535.2023.10141761","DOI":"10.1109\/InCACCT57535.2023.10141761"},{"issue":"4","key":"3552_CR3","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1007\/s10618-021-00762-8","volume":"35","author":"RM Pereira","year":"2021","unstructured":"Pereira, R.M., Costa, Y.M., Silla, C.N., Jr.: Handling imbalance in hierarchical classification problems using local classifiers approaches. Data Min. Knowl. Discov. 35(4), 1564\u20131621 (2021). https:\/\/doi.org\/10.1007\/s10618-021-00762-8","journal-title":"Data Min. Knowl. Discov."},{"key":"3552_CR4","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2026895","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Li, S., Hao, J., Hu, J., Pan, M.: An efficient and fast model reduced kernel KNN for human activity recognition. J. Adv. Transp. (2021). https:\/\/doi.org\/10.1155\/2021\/2026895","journal-title":"J. Adv. Transp."},{"key":"3552_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-03862-5","author":"D Thakur","year":"2022","unstructured":"Thakur, D., Biswas, S.: Guided regularized random forest feature selection for smartphone based human activity recognition. J. Ambient. Intell. Humaniz. Comput. (2022). https:\/\/doi.org\/10.1007\/s12652-022-03862-5","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"3552_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2022.100190","volume":"15","author":"N Halim","year":"2022","unstructured":"Halim, N.: Stochastic recognition of human daily activities via hybrid descriptors and random forest using wearable sensors. Array 15, 100190 (2022)","journal-title":"Array"},{"key":"3552_CR7","doi-asserted-by":"publisher","first-page":"13433","DOI":"10.1007\/s12652-022-03798-w","volume":"14","author":"Y Nawal","year":"2022","unstructured":"Nawal, Y., Oussalah, M., Fergani, B., Fleury, A.: New incremental SVM algorithms for human activity recognition in smart homes. J. Ambient Intell. Hum. Comput. 14, 13433\u201313450 (2022)","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"3552_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F.J., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 115 (2016)","journal-title":"Sensors"},{"issue":"6","key":"3552_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-020-02005-7","volume":"51","author":"L Chen","year":"2021","unstructured":"Chen, L., Liu, X., Peng, L., Wu, M.: Deep learning based multimodal complex human activity recognition using wearable devices. Appl. Intell. 51(6), 1\u201314 (2021)","journal-title":"Appl. Intell."},{"issue":"30","key":"3552_CR10","doi-asserted-by":"publisher","first-page":"36159","DOI":"10.1007\/s11042-021-11363-4","volume":"80","author":"R Huan","year":"2021","unstructured":"Huan, R., Zhan, Z., Luoqi, G., Chi, K., Chen, P., Liang, R.: A hybrid CNN and BLSTM network for human complex activity recognition with multi-feature fusion. Multimedia Tools Appl. 80(30), 36159\u201336182 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"3552_CR11","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, Sakorn, Jitpattanakul, A.: LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors 21(5), 1636 (2021)","DOI":"10.3390\/s21051636"},{"key":"3552_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/2132138","volume":"10","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., An, Y., Wang, K., Li, S.: Wearable sensor-based human activity recognition using hybrid deep learning techniques. Secur. Commun. Netw. 10, 1\u201312 (2020). https:\/\/doi.org\/10.1155\/2020\/2132138","journal-title":"Secur. Commun. Netw."},{"issue":"5","key":"3552_CR13","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.3390\/s22051911","volume":"22","author":"I Dirgov\u00e1 Lupt\u00e1kov\u00e1","year":"2022","unstructured":"Dirgov\u00e1 Lupt\u00e1kov\u00e1, I., Kubov\u010d\u00edk, M., Posp\u00edchal, J.: Wearable sensor-based human activity recognition with transformer model. Sensors 22(5), 1911 (2022). https:\/\/doi.org\/10.3390\/s22051911","journal-title":"Sensors"},{"key":"3552_CR14","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s12530-022-09480-y","volume":"14","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Wang, W., An, A., et al.: A human activity recognition method using wearable sensors based on convtransformer model. Evol. Syst. 14, 939\u2013955 (2023)","journal-title":"Evol. Syst."},{"key":"3552_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-020-02089-1","volume":"51","author":"W Zheng","year":"2021","unstructured":"Zheng, W., Zhao, H.: Cost-sensitive hierarchical classification via multi-scale information entropy for data with an imbalanced distribution. Appl. Intell. 51, 1\u201313 (2021)","journal-title":"Appl. Intell."},{"issue":"10","key":"3552_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0075196","volume":"8","author":"H Leutheuser","year":"2013","unstructured":"Leutheuser, H., Schuldhaus, D., Eskofier, B.M.: Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 8(10), 1\u201311 (2013)","journal-title":"PLoS ONE"},{"key":"3552_CR17","doi-asserted-by":"crossref","unstructured":"Fazli, M., Kowsari, K., Gharavi, E., Barnes, L., Doryab, A.: HHAR-Net: Hierarchical human activity recognition using neural networks (2020)","DOI":"10.1007\/978-3-030-68449-5_6"},{"key":"3552_CR18","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.3390\/s23031390","volume":"23","author":"N Manouchehri","year":"2023","unstructured":"Manouchehri, N., Bouguila, N.: Human activity recognition with an HMM-based generative model. Sensors 23, 1390 (2023). https:\/\/doi.org\/10.3390\/s23031390","journal-title":"Sensors"},{"key":"3552_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-57912-3","author":"X Wei","year":"2024","unstructured":"Wei, X., Wang, Z.: TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network. Sci. Rep. (2024). https:\/\/doi.org\/10.1038\/s41598-024-57912-3","journal-title":"Sci. Rep."},{"key":"3552_CR20","doi-asserted-by":"publisher","first-page":"145271","DOI":"10.1109\/ACCESS.2021.3122298","volume":"9","author":"NT Hoai Thu","year":"2021","unstructured":"Hoai Thu, N.T., Han, D.S.: HIHAR: a hierarchical hybrid deep learning architecture for wearable sensor-based human activity recognition. IEEE Access 9, 145271\u2013145281 (2021)","journal-title":"IEEE Access"},{"issue":"3","key":"3552_CR21","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3390\/informatics9030056","volume":"9","author":"YJ Luwe","year":"2022","unstructured":"Luwe, Y.J., Lee, C.P., Lim, K.M.: Wearable sensor-based human activity recognition with hybrid deep learning model. Informatics 9(3), 56 (2022)","journal-title":"Informatics"},{"key":"3552_CR22","doi-asserted-by":"publisher","first-page":"20620","DOI":"10.1038\/s41598-022-24887-y","volume":"12","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Cao, K., Lu, L., Deng, T.: A multi-scale feature extraction fusion model for human activity recognition. Sci. Rep. 12, 20620 (2022)","journal-title":"Sci. Rep."},{"key":"3552_CR23","doi-asserted-by":"publisher","DOI":"10.1088\/2631-8695\/acd98c","volume":"5","author":"U Verma","year":"2023","unstructured":"Verma, U., Tyagi, P., Aneja, M.K.: Multi-branch CNN GRU with attention mechanism for human action recognition. Eng. Res. Express 5, 025055 (2023)","journal-title":"Eng. Res. Express"},{"key":"3552_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2010.112","author":"D Cook","year":"2010","unstructured":"Cook, D.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. (2010). https:\/\/doi.org\/10.1109\/MIS.2010.112","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"3552_CR25","first-page":"321","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321\u2013357 (2002)","journal-title":"J. Artif. Int. Res."},{"key":"3552_CR26","doi-asserted-by":"publisher","unstructured":"Slim, S.O., Atia, A., Marwa, M.A., Mostafa, M.-S.: Survey on human activity recognition based on acceleration data. Int. J. Adv. Comput. Sci. Appl. 1, 12\u201356 (2019). https:\/\/doi.org\/10.14569\/IJACSA.2019.0100311","DOI":"10.14569\/IJACSA.2019.0100311"},{"issue":"6","key":"3552_CR27","first-page":"1","volume":"15","author":"C Li","year":"2021","unstructured":"Li, C., Tong, C.L., Niu, D., Jiang, B., Zuo, X., Cheng, L., Xiong, J., Yang, J.: Similarity embedding networks for robust human activity recognition. ACM Trans. Knowl. Discov. Data (TKDD) 15(6), 1\u201317 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"3552_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., Zhang, Z., Bao, J., Song, Y.: Human activity recognition based on time series analysis using U-Net. arXiv (2018)","DOI":"10.1109\/ACCESS.2019.2920969"},{"key":"3552_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3542819","volume":"21","author":"F Daghero","year":"2022","unstructured":"Daghero, F., Burrello, A., Xie, C., Castellano, M., Gandolfi, L., Calimera, A., Macii, E., Poncino, M., Jahier Pagliari, D.: Human activity recognition on microcontrollers with quantized and adaptive deep neural networks. ACM Trans. Embed. Comput. Syst. 21, 1\u201328 (2022)","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"3552_CR30","doi-asserted-by":"crossref","unstructured":"Xu, C., Chai, D., He, J., Zhang, X., Duan, S.: InnoHAR: a deep neural network for complex human activity recognition. IEEE Access (2018)","DOI":"10.1109\/ACCESS.2018.2890675"},{"key":"3552_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2021.3078184","volume":"9","author":"M Ronald","year":"2021","unstructured":"Ronald, M., Poulose, A., Han, D.S.: iSPLInception: an inception-resnet deep learning architecture for human activity recognition. IEEE Access 9, 1 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3078184","journal-title":"IEEE Access"},{"key":"3552_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119419","volume":"216","author":"TR Mim","year":"2023","unstructured":"Mim, T.R., Amatullah, M., Afreen, S., Yousuf, M.A., Uddin, S., Alyami, S.A., Hasan, K.F., Moni, M.A.: GRU-INC: an inception-attention based approach using GRU for human activity recognition. Expert Syst. Appl. 216, 119419 (2023)","journal-title":"Expert Syst. Appl."},{"key":"3552_CR33","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNs for time series classification. Neural Netw. 116, 237\u2013245 (2019)","journal-title":"Neural Netw."},{"key":"3552_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/7316954","volume":"2018","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Yang, R., Chevalier, G., Xu, X., Zhang, Z.: Deep residual BIDIR-LSTM for human activity recognition using wearable sensors. Math. Probl. Eng. 2018, 1\u201313 (2018)","journal-title":"Math. Probl. Eng."},{"key":"3552_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116764","volume":"198","author":"C Han","year":"2022","unstructured":"Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., He, J.: Human activity recognition using wearable sensors by heterogeneous convolutional neural networks. Expert Syst. Appl. 198, 116764 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"3552_CR36","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/bios12060393","volume":"12","author":"D Bhattacharya","year":"2022","unstructured":"Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M.F., Singh, P.K.: ENSEM-HAR: an ensemble deep learning model for smartphone sensor-based human activity recognition for measurement of elderly health monitoring. Biosensors 12(6), 393 (2022)","journal-title":"Biosensors"},{"issue":"7","key":"3552_CR37","doi-asserted-by":"publisher","first-page":"5165","DOI":"10.1007\/s00521-022-07911-0","volume":"35","author":"A Sarkar","year":"2023","unstructured":"Sarkar, A., Hossain, R., Sabbir, S.K.: Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm. Neural Comput. Appl. 35(7), 5165\u20135191 (2023)","journal-title":"Neural Comput. Appl."},{"key":"3552_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116764","volume":"198","author":"C Han","year":"2022","unstructured":"Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., He, J.: Human activity recognition using wearable sensors by heterogeneous convolutional neural networks. Expert Syst. Appl. 198, 116764 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"16","key":"3552_CR39","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.3390\/electronics11162526","volume":"11","author":"Y Li","year":"2022","unstructured":"Li, Y., Wang, L., Liu, F.: Multi-branch attention-based grouped convolution network for human activity recognition using inertial sensors. Electronics 11(16), 2526 (2022)","journal-title":"Electronics"},{"issue":"19","key":"3552_CR40","doi-asserted-by":"publisher","first-page":"7446","DOI":"10.3390\/s22197446","volume":"22","author":"B Zhou","year":"2022","unstructured":"Zhou, B., Wang, C., Huan, Z., Li, Z., Chen, Y., Gao, G., Li, H., Dong, C., Liang, J.: A novel segmentation scheme with multi-probability threshold for human activity recognition using wearable sensors. Sensors 22(19), 7446 (2022)","journal-title":"Sensors"},{"key":"3552_CR41","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-981-16-0575-8_9","volume-title":"Deep Learning for Human Activity Recognition","author":"D Bouchabou","year":"2021","unstructured":"Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., Kanellos, I.: Fully convolutional network bootstrapped by word encoding and embedding for activity recognition in smart homes. In: Li, X., Wu, M., Chen, Z., Zhang, L. (eds.) Deep Learning for Human Activity Recognition, pp. 111\u2013125. Springer, Singapore (2021)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03552-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03552-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03552-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T07:27:27Z","timestamp":1730705247000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03552-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,21]]},"references-count":41,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["3552"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03552-z","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,21]]},"assertion":[{"value":"2 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The datasets used in this research are publicly available.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical\/Informed consent for data"}}]}}