{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:32:51Z","timestamp":1775068371244,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006196","name":"University of Oulu","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006196","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Human Activity Recognition (HAR) has long been a research hotspot in the pattern recognition field due to its extensive applications across various domains. The core idea of HAR is to train machines to identify human physical activities using data recorded by various sensor modalities, which is particularly useful in areas such as e-health, where fall detection and remote patient health monitoring are of paramount importance. Traditional machine learning algorithms, such as Support Vector Machines (SVM), have demonstrated strong performance in HAR state-of-the-art literature; however, they rely on manual feature extraction, which is time-consuming and requires domain expertise. In contrast, recent advancements have established Convolutional Neural Networks (CNNs) as powerful tools that automatically extract optimal features directly from raw data, eliminating the need for manual intervention. In this paper, we introduce a hybrid model called DeepF-SVM to enhance the performance of CNNs and address the reliance of SVM on domain expertise. First, a one-dimensional CNN with three convolutional layers is trained on raw sensor data to extract deep features (DeepF). Then, an SVM classifier with an RBF kernel replaces the final dense layer of the CNN, taking the DeepF from the preceding layer as input for activity classification. Experiments are conducted on three publicly available datasets\u2013UCI HAR, UniMiB SHAR, and PAMAP2\u2013to evaluate the performance of the proposed approach. The DeepF-SVM model achieved accuracy scores of 96.44%, 93.57%, and 98.48% on the above three datasets, respectively, with inference times of 0.3175s for UCI HAR, 1.1168s for UniMiB SHAR, and 0.3672s for PAMAP2. The results demonstrate that the developed DeepF-SVM model outperformed both standalone CNN and standalone SVM models, confirming its high effectiveness and potential prospects in HAR tasks.<\/jats:p>","DOI":"10.1007\/s10586-025-05636-y","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T20:17:13Z","timestamp":1759177033000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DeepF-SVM: A\u00a0new hybrid deep learning model for enhanced sensor-based human activity recognition"],"prefix":"10.1007","volume":"28","author":[{"given":"Imene","family":"Charabi","sequence":"first","affiliation":[]},{"given":"M\u2019hamed Bilal","family":"Abidine","sequence":"additional","affiliation":[]},{"given":"Belkacem","family":"Fergani","sequence":"additional","affiliation":[]},{"given":"Mourad","family":"Oussalah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"5636_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-024-04357-y","volume":"27","author":"A Hatem","year":"2024","unstructured":"Hatem, A., Gomaa, I., Zayed, H., Taha, M.: Iot-based ehealth using blockchain technology: a survey. Cluster Computing 27, 1\u201328 (2024). https:\/\/doi.org\/10.1007\/s10586-024-04357-y","journal-title":"Cluster Computing"},{"key":"5636_CR2","doi-asserted-by":"publisher","first-page":"3291","DOI":"10.1007\/s10586-023-04047-1","volume":"26","author":"W Bovenizer","year":"2023","unstructured":"Bovenizer, W., Chetthamrongchai, P.: A comprehensive systematic and bibliometric review of the iot-based healthcare systems. Cluster Computing 26, 3291\u20133317 (2023). https:\/\/doi.org\/10.1007\/s10586-023-04047-1","journal-title":"Cluster Computing"},{"key":"5636_CR3","doi-asserted-by":"publisher","unstructured":"Angelucci, A., Greco, M., Cecconi, M., Aliverti, A.: Wearable devices for patient monitoring in the intensive care unit. Intensive Care Medicine Experimental 13 (2025). https:\/\/doi.org\/10.1186\/s40635-025-00738-8","DOI":"10.1186\/s40635-025-00738-8"},{"key":"5636_CR4","doi-asserted-by":"publisher","unstructured":"Hiremath, S.K., Pl\u00f6tz, T.: Maintenance required: Updating and extending bootstrapped human activity recognition systems for smart homes. In: 2024 International Conference on Activity and Behavior Computing (ABC), pp. 1\u201313 (2024). https:\/\/doi.org\/10.1109\/ABC61795.2024.10651685","DOI":"10.1109\/ABC61795.2024.10651685"},{"key":"5636_CR5","doi-asserted-by":"publisher","first-page":"13433","DOI":"10.1007\/s12652-022-03798-w","volume":"14","author":"Y Nawal","year":"2023","unstructured":"Nawal, Y., Oussalah, M., Fergani, B., Fleury, A.: New incremental svm algorithms for human activity recognition in smart homes. Journal of Ambientient Intelligence and Humanized Computing 14, 13433\u201313450 (2023). https:\/\/doi.org\/10.1007\/s12652-022-03798-w","journal-title":"Journal of Ambientient Intelligence and Humanized Computing"},{"key":"5636_CR6","doi-asserted-by":"publisher","unstructured":"Al\u00a0Farid, F., Bari, A., Miah, A.S.M., Mansor, S., Uddin, J., Kumaresan, S.P.: A structured and methodological review on multi-view human activity recognition for ambient assisted living. Journal of Imaging 11(6) (2025) https:\/\/doi.org\/10.3390\/jimaging11060182","DOI":"10.3390\/jimaging11060182"},{"key":"5636_CR7","doi-asserted-by":"publisher","unstructured":"Su, C., Wei, J., Lin, D., Kong, L., Guan, Y.: A novel model for fall detection and action recognition combined lightweight 3d-cnn and convolutional lstm networks. Pattern Analysis and Applications 27 (2024). https:\/\/doi.org\/10.1007\/s10044-024-01224-9","DOI":"10.1007\/s10044-024-01224-9"},{"key":"5636_CR8","doi-asserted-by":"publisher","unstructured":"Li, Y., Liu, P., Fang, Y., Wu, X., Xie, Y., Xu, Z., Ren, H., Jing, F.: A decade of progress in wearable sensors for fall detection (2015\u20132024): A network-based visualization review. Sensors 25(7) (2025). https:\/\/doi.org\/10.3390\/s25072205","DOI":"10.3390\/s25072205"},{"key":"5636_CR9","doi-asserted-by":"publisher","unstructured":"Liu, M.-C., Hsu, F.-R., Huang, C.-H.: Complex event recognition and anomaly detection with event behavior model. Pattern Anal. Appl. 27(2) (2024). https:\/\/doi.org\/10.1007\/s10044-024-01275-y","DOI":"10.1007\/s10044-024-01275-y"},{"key":"5636_CR10","doi-asserted-by":"publisher","unstructured":"Gowda, D.D., Kaur, M., Srinivas, D., Xxx, K., Shekhar, R.: AIoT Integration Advancements and Challenges in Smart Sensing Technologies for Smart Devices, pp. 42\u201365 (2024). https:\/\/doi.org\/10.4018\/979-8-3693-0786-1.ch003","DOI":"10.4018\/979-8-3693-0786-1.ch003"},{"issue":"28","key":"5636_CR11","doi-asserted-by":"publisher","first-page":"20463","DOI":"10.1007\/s00521-023-08863-9","volume":"35","author":"W Gomaa","year":"2023","unstructured":"Gomaa, W., Khamis, M.A.: A perspective on human activity recognition from inertial motion data. Neural Comput. Appl. 35(28), 20463\u201320568 (2023). https:\/\/doi.org\/10.1007\/s00521-023-08863-9","journal-title":"Neural Comput. Appl."},{"key":"5636_CR12","doi-asserted-by":"publisher","unstructured":"Khaked, A.A., Oishi, N., Roggen, D., Lago, P.: In shift and in variance: Assessing the robustness of har deep learning models against variability. Sensors 25(2) (2025) https:\/\/doi.org\/10.3390\/s25020430","DOI":"10.3390\/s25020430"},{"key":"5636_CR13","unstructured":"Bouchabou, D., Lohr, C., Kanellos, I., Nguyen, S.M.: Human Activity Recognition (HAR) in Smart Homes (2021). https:\/\/arxiv.org\/abs\/2112.11232"},{"key":"5636_CR14","doi-asserted-by":"publisher","unstructured":"Gu, F., Chung, M.-H., Chignell, M., Valaee, S., Zhou, B., Liu, X.: A survey on deep learning for human activity recognition. ACM Computing Surveys 54 (2021) https:\/\/doi.org\/10.1145\/3472290","DOI":"10.1145\/3472290"},{"key":"5636_CR15","doi-asserted-by":"publisher","unstructured":"Hossen, M.A., Abas, P.E.: Machine learning for human activity recognition: State-of-the-art techniques and emerging trends. Journal of Imaging 11(3) (2025) https:\/\/doi.org\/10.3390\/jimaging11030091","DOI":"10.3390\/jimaging11030091"},{"key":"5636_CR16","doi-asserted-by":"publisher","first-page":"3622","DOI":"10.3390\/math12223622","volume":"12","author":"P Thottempudi","year":"2024","unstructured":"Thottempudi, P., Acharya, B., Moreira, F.: High-performance real-time human activity recognition using machine learning. Mathematics 12, 3622 (2024). https:\/\/doi.org\/10.3390\/math12223622","journal-title":"Mathematics"},{"key":"5636_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s10044-016-0570-y","volume":"21","author":"M Abidine","year":"2018","unstructured":"Abidine, M., Fergani, L., Fergani, B., Oussalah, M.: The joint use of sequence features combination and modified weighted svm for improving daily activity recognition. Pattern Analysis and Applications 21, 119\u2013138 (2018). https:\/\/doi.org\/10.1007\/s10044-016-0570-y","journal-title":"Pattern Analysis and Applications"},{"key":"5636_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123143","volume":"246","author":"V Dentamaro","year":"2024","unstructured":"Dentamaro, V., Gattulli, V., Impedovo, D., Manca, F.: Human activity recognition with smartphone-integrated sensors: A survey. Expert Systems with Applications 246, 123143 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.123143","journal-title":"Expert Systems with Applications"},{"key":"5636_CR19","doi-asserted-by":"publisher","unstructured":"Kumar, P., Chauhan, S., Awasthi, L.: Human activity recognition (har) using deep learning: Review, methodologies, progress and future research directions. Archives of Computational Methods in Engineering 31 (2023) https:\/\/doi.org\/10.1007\/s11831-023-09986-x","DOI":"10.1007\/s11831-023-09986-x"},{"issue":"2","key":"5636_CR20","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/make6020040","volume":"6","author":"M Kaseris","year":"2024","unstructured":"Kaseris, M., Kostavelis, I., Malassiotis, S.: A comprehensive survey on deep learning methods in human activity recognition. Machine Learning and Knowledge Extraction 6(2), 842\u2013876 (2024). https:\/\/doi.org\/10.3390\/make6020040","journal-title":"Machine Learning and Knowledge Extraction"},{"issue":"9","key":"5636_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3649448","volume":"56","author":"N Mohammadi Foumani","year":"2024","unstructured":"Mohammadi Foumani, N., Miller, L., Tan, C.W., Webb, G.I., Forestier, G., Salehi, M.: Deep learning for time series classification and extrinsic regression: A current survey. ACM Computing Surveys 56(9), 1\u201345 (2024)","journal-title":"ACM Computing Surveys"},{"key":"5636_CR22","doi-asserted-by":"publisher","first-page":"9893","DOI":"10.1109\/access.2018.2890675","volume":"7","author":"C Xu","year":"2019","unstructured":"Xu, C., Chai, D., He, J., Zhang, X., Duan, S.: Innohar: a deep neural network for complex human activity recognition. IEEE Access 7, 9893\u20139902 (2019). https:\/\/doi.org\/10.1109\/access.2018.2890675","journal-title":"IEEE Access"},{"key":"5636_CR23","doi-asserted-by":"publisher","unstructured":"Yu, Z., Zahid, A., Taylor, W., Heidari, H., Imran, M.A., Abbasi, Q.H.: Multi-sensing data fusion for human activity recognition based on neuromorphic computing. 2021 IEEE USNC-URSI Radio Science Meeting (Joint With AP-S Symposium) (2021) https:\/\/doi.org\/10.23919\/usnc-ursi51813.2021.9703558","DOI":"10.23919\/usnc-ursi51813.2021.9703558"},{"key":"5636_CR24","doi-asserted-by":"publisher","unstructured":"Thakur, D., Biswas, S., Ho, E., Chattopadhyay, S.: Convae-lstm: Convolutional autoencoder long short-term memory network for smartphone-based human activity recognition. IEEE Access PP, 1\u20131 (2022) https:\/\/doi.org\/10.1109\/ACCESS.2022.3140373","DOI":"10.1109\/ACCESS.2022.3140373"},{"key":"5636_CR25","doi-asserted-by":"publisher","unstructured":"Abdellatef, E., Al-Makhlasawy, R., Shalaby, W.: Detection of human activities using multi-layer convolutional neural network. Scientific Reports 15 (2025) https:\/\/doi.org\/10.1038\/s41598-025-90307-6","DOI":"10.1038\/s41598-025-90307-6"},{"key":"5636_CR26","doi-asserted-by":"publisher","first-page":"36372","DOI":"10.1109\/ACCESS.2024.3373199","volume":"12","author":"M Karim","year":"2024","unstructured":"Karim, M., Khalid, S., Aleryani, A., Khan, J., Ullah, I., Ali, Z.: Human action recognition systems: A review of the trends and state-of-the-art. IEEE Access 12, 36372\u201336390 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3373199","journal-title":"IEEE Access"},{"key":"5636_CR27","doi-asserted-by":"publisher","unstructured":"Shdefat, A., Mostafa, N., Al-Arnaout, Z., Kotb, Y., Alabed, S.: Optimizing har systems: Comparative analysis of enhanced svm and k-nn classifiers. International Journal of Computational Intelligence Systems 17 (2024) https:\/\/doi.org\/10.1007\/s44196-024-00554-0","DOI":"10.1007\/s44196-024-00554-0"},{"key":"5636_CR28","doi-asserted-by":"publisher","unstructured":"Nia, N.G., Kaplanoglu, E., Nasab, A., Qin, H.: Human activity recognition using machine learning algorithms based on imu data. In: 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1\u20138 (2023). https:\/\/doi.org\/10.1109\/BioSMART58455.2023.10162095","DOI":"10.1109\/BioSMART58455.2023.10162095"},{"key":"5636_CR29","doi-asserted-by":"publisher","first-page":"84121","DOI":"10.1007\/s11042-024-18993-4","volume":"83","author":"U Thakur","year":"2024","unstructured":"Thakur, U., Prajapati, A., Vidyarthi, A.: A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine. Multimedia Tools and Applications 83, 84121\u201384140 (2024). https:\/\/doi.org\/10.1007\/s11042-024-18993-4","journal-title":"Multimedia Tools and Applications"},{"key":"5636_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107681","volume":"129","author":"D Thakur","year":"2024","unstructured":"Thakur, D., Biswas, S.: Permutation importance based modified guided regularized random forest in human activity recognition with smartphone. Engineering Applications of Artificial Intelligence 129, 107681 (2024). https:\/\/doi.org\/10.1016\/j.engappai.2023.107681","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"5636_CR31","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.aej.2024.01.030","volume":"91","author":"L Zhang","year":"2024","unstructured":"Zhang, L., Yu, J., Gao, Z., Ni, Q.: A multi-channel hybrid deep learning framework for multi-sensor fusion enabled human activity recognition. Alexandria Engineering Journal 91, 472\u2013485 (2024). https:\/\/doi.org\/10.1016\/j.aej.2024.01.030","journal-title":"Alexandria Engineering Journal"},{"key":"5636_CR32","doi-asserted-by":"publisher","unstructured":"Fatima, I., Farhan, A.A., Tamoor, M., Rehman, S., Alhulayyil, H.A., Tariq, F.: Dischar: A discrete approach to enhance human activity recognition in cyber physical systems: Smart homes. Computers 13(11) (2024) https:\/\/doi.org\/10.3390\/computers13110300","DOI":"10.3390\/computers13110300"},{"key":"5636_CR33","doi-asserted-by":"publisher","first-page":"22581","DOI":"10.1038\/s41598-023-49739-1","volume":"13","author":"R Raj","year":"2023","unstructured":"Raj, R., Kos, A.: An improved human activity recognition technique based on convolutional neural network. Scientific Reports 13, 22581 (2023). https:\/\/doi.org\/10.1038\/s41598-023-49739-1","journal-title":"Scientific Reports"},{"issue":"2","key":"5636_CR34","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TIE.2022.3161812","volume":"70","author":"Y Tang","year":"2023","unstructured":"Tang, Y., Zhang, L., Min, F., He, J.: Multiscale deep feature learning for human activity recognition using wearable sensors. IEEE Transactions on Industrial Electronics 70(2), 2106\u20132116 (2023). https:\/\/doi.org\/10.1109\/TIE.2022.3161812","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"2","key":"5636_CR35","doi-asserted-by":"publisher","first-page":"13636","DOI":"10.1016\/j.heliyon.2023.e13636","volume":"9","author":"WN Ismail","year":"2023","unstructured":"Ismail, W.N., Alsalamah, H.A., Hassan, M.M., Mohamed, E.: Auto-har: An adaptive human activity recognition framework using an automated cnn architecture design. Heliyon 9(2), 13636 (2023). https:\/\/doi.org\/10.1016\/j.heliyon.2023.e13636","journal-title":"Heliyon"},{"key":"5636_CR36","doi-asserted-by":"publisher","unstructured":"Huang, W., Zhang, L., Teng, Q., Song, C., He, J.: The convolutional neural networks training with channel-selectivity for human activity recognition based on sensors. IEEE Journal of Biomedical and Health Informatics PP, 1\u20131 (2021) https:\/\/doi.org\/10.1109\/JBHI.2021.3092396","DOI":"10.1109\/JBHI.2021.3092396"},{"key":"5636_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107728","volume":"111","author":"W Gao","year":"2021","unstructured":"Gao, W., Zhang, L., Teng, Q., He, J., Wu, H.: Danhar: Dual attention network for multimodal human activity recognition using wearable sensors. Applied Soft Computing 111, 107728 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107728","journal-title":"Applied Soft Computing"},{"key":"5636_CR38","doi-asserted-by":"publisher","unstructured":"Luwe, Y.J., Lee, C.P., Lim, K.M.: Wearable sensor-based human activity recognition with hybrid deep learning model. Informatics 9(3) (2022) https:\/\/doi.org\/10.3390\/informatics9030056","DOI":"10.3390\/informatics9030056"},{"key":"5636_CR39","doi-asserted-by":"publisher","unstructured":"Thakur, D., Roy, S., Biswas, S., Ho, E., Chattopadhyay, S., Shetty, S.: A novel smartphone-based human activity recognition approach using convolutional autoencoder long short-term memory network, pp. 146\u2013153 (2023). https:\/\/doi.org\/10.1109\/IRI58017.2023.00032","DOI":"10.1109\/IRI58017.2023.00032"},{"key":"5636_CR40","doi-asserted-by":"publisher","unstructured":"Hossain\u00a0Shuvo, M.M., Ahmed, N., Nouduri, K., Palaniappan, K.: A hybrid approach for human activity recognition with support vector machine and 1d convolutional neural network. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1\u20135 (2020). https:\/\/doi.org\/10.1109\/AIPR50011.2020.9425332","DOI":"10.1109\/AIPR50011.2020.9425332"},{"key":"5636_CR41","doi-asserted-by":"publisher","unstructured":"Chui, K.T., Gupta, B.B., Torres-Ruiz, M., Arya, V., Alhalabi, W., Zamzami, I.F.: A convolutional neural network-based feature extraction and weighted twin support vector machine algorithm for context-aware human activity recognition. Electronics 12(8) (2023) https:\/\/doi.org\/10.3390\/electronics12081915","DOI":"10.3390\/electronics12081915"},{"key":"5636_CR42","doi-asserted-by":"publisher","unstructured":"Wagner, D., Kalischewski, K., Velten, J., Kummert, A.: Activity recognition using inertial sensors and a 2-d convolutional neural network. In: 2017 10th International Workshop on Multidimensional (nD) Systems (nDS), pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/NDS.2017.8070615","DOI":"10.1109\/NDS.2017.8070615"},{"key":"5636_CR43","doi-asserted-by":"publisher","first-page":"282","DOI":"10.4028\/www.scientific.net\/AST.105.282","volume":"105","author":"V Athavale","year":"2021","unstructured":"Athavale, V., Kumar, D., Gupta, S.: Human action recognition using cnn-svm model. Advances in Science and Technology 105, 282\u2013290 (2021). https:\/\/doi.org\/10.4028\/www.scientific.net\/AST.105.282","journal-title":"Advances in Science and Technology"},{"key":"5636_CR44","doi-asserted-by":"publisher","unstructured":"Shahid, S.M., Ko, S., Kwon, S.: Performance comparison of 1d and 2d convolutional neural networks for real-time classification of time series sensor data. In: 2022 International Conference on Information Networking (ICOIN), pp. 507\u2013511 (2022). https:\/\/doi.org\/10.1109\/ICOIN53446.2022.9687284","DOI":"10.1109\/ICOIN53446.2022.9687284"},{"key":"5636_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1d convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing 151, 107398 (2021). https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398","journal-title":"Mechanical Systems and Signal Processing"},{"key":"5636_CR46","doi-asserted-by":"publisher","unstructured":"Guido, R., Ferrisi, S., Lofaro, D., Conforti, D.: An overview on the advancements of support vector machine models in healthcare applications: A review. Information 15(4) (2024) https:\/\/doi.org\/10.3390\/info15040235","DOI":"10.3390\/info15040235"},{"key":"5636_CR47","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.N.: Support-vector networks. Machine Learning 20, 273\u2013297 (1995)","journal-title":"Machine Learning"},{"key":"5636_CR48","doi-asserted-by":"publisher","unstructured":"Krichen, M.: Convolutional neural networks: A survey. Computers 12(8) (2023) https:\/\/doi.org\/10.3390\/computers12080151","DOI":"10.3390\/computers12080151"},{"key":"5636_CR49","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","volume":"408","author":"J Cervantes","year":"2020","unstructured":"Cervantes, J., Garcia-Lamont, F., Rodr\u00edguez-Mazahua, L., Lopez, A.: A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408, 189\u2013215 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.10.118","journal-title":"Neurocomputing"},{"key":"5636_CR50","doi-asserted-by":"publisher","unstructured":"Kiranyaz, S., Ince, Y., Abdeljaber, O., Avci, O., Gabbouj, G.: 1-d convolutional neural networks for signal processing applications. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8360\u20138364 (2019). https:\/\/doi.org\/10.1109\/ICASSP.2019.8682194","DOI":"10.1109\/ICASSP.2019.8682194"},{"key":"5636_CR51","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., etal: A public domain dataset for human activity recognition using smartphones. In: Esann, vol. 3, pp. 3\u20134 (2013)"},{"key":"5636_CR52","doi-asserted-by":"publisher","unstructured":"Micucci, D., Mobilio, M., Napoletano, P.: Unimib shar: A dataset for human activity recognition using acceleration data from smartphones. Applied Sciences 7(10) (2017) https:\/\/doi.org\/10.3390\/app7101101","DOI":"10.3390\/app7101101"},{"key":"5636_CR53","doi-asserted-by":"publisher","unstructured":"Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108\u2013109 (2012). https:\/\/doi.org\/10.1109\/ISWC.2012.13","DOI":"10.1109\/ISWC.2012.13"},{"key":"5636_CR54","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"5636_CR55","doi-asserted-by":"publisher","unstructured":"Challa, S., Kumar, A., Semwal, V.: A multibranch cnn-bilstm model for human activity recognition using wearable sensor data. The Visual Computer 38 (2021) https:\/\/doi.org\/10.1007\/s00371-021-02283-3","DOI":"10.1007\/s00371-021-02283-3"},{"key":"5636_CR56","doi-asserted-by":"publisher","unstructured":"Nafea, O., Abdul, W., Muhammad, G.: Multi-sensor human activity recognition using cnn and gru. International Journal of Multimedia Information Retrieval 11 (2022) https:\/\/doi.org\/10.1007\/s13735-022-00234-9","DOI":"10.1007\/s13735-022-00234-9"},{"issue":"7","key":"5636_CR57","doi-asserted-by":"publisher","first-page":"7188","DOI":"10.1109\/JSEN.2023.3242603","volume":"23","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Xu, H., Liu, Y., Wang, M., Wang, Y., Yang, Y., Zhou, S., Zeng, J., Xu, J., Li, S., Li, J.: A novel deep multifeature extraction framework based on attention mechanism using wearable sensor data for human activity recognition. IEEE Sensors Journal 23(7), 7188\u20137198 (2023). https:\/\/doi.org\/10.1109\/JSEN.2023.3242603","journal-title":"IEEE Sensors Journal"},{"key":"5636_CR58","doi-asserted-by":"publisher","unstructured":"Dubey, A., Zacharias, J.: Imu data based har using hybrid model of cnn and stacked lstm. In: 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI), pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/APCI61480.2024.10616429","DOI":"10.1109\/APCI61480.2024.10616429"},{"key":"5636_CR59","doi-asserted-by":"publisher","unstructured":"Thakur, D., Roy, S., Biswas, S., Ho, E., Chattopadhyay, S., Shetty, S.: A novel smartphone-based human activity recognition approach using convolutional autoencoder long short-term memory network, pp. 146\u2013153 (2023). https:\/\/doi.org\/10.1109\/IRI58017.2023.00032","DOI":"10.1109\/IRI58017.2023.00032"},{"key":"5636_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JSEN.2022.3172603","volume":"22","author":"D Thakur","year":"2022","unstructured":"Thakur, D., Biswas, S.: Attention-based deep learning framework for hemiplegic gait prediction with smartphone sensors. IEEE Sensors Journal 22, 1\u20131 (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3172603","journal-title":"IEEE Sensors Journal"},{"issue":"2","key":"5636_CR61","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1109\/JBHI.2024.3488528","volume":"29","author":"Q Teng","year":"2025","unstructured":"Teng, Q., Li, W., Hu, G., Shu, Y., Liu, Y.: Innovative dual-decoupling cnn with layer-wise temporal-spatial attention for sensor-based human activity recognition. IEEE Journal of Biomedical and Health Informatics 29(2), 1035\u20131048 (2025). https:\/\/doi.org\/10.1109\/JBHI.2024.3488528","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"5636_CR62","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67\u201382 (1997). https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Transactions on Evolutionary Computation"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05636-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05636-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05636-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:06:30Z","timestamp":1764237990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05636-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":62,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5636"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05636-y","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"10 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have no Competing Interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"910"}}