{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T20:59:54Z","timestamp":1776459594088,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pervasive computing, human\u2013computer interaction, human behavior analysis, and human activity recognition (HAR) fields have grown significantly. Deep learning (DL)-based techniques have recently been effectively used to predict various human actions using time series data from wearable sensors and mobile devices. The management of time series data remains difficult for DL-based techniques, despite their excellent performance in activity detection. Time series data still has several problems, such as difficulties in heavily biased data and feature extraction. For HAR, an ensemble of Deep SqueezeNet (SE) and bidirectional long short-term memory (BiLSTM) with improved flower pollination optimization algorithm (IFPOA) is designed to construct a reliable classification model utilizing wearable sensor data in this research. The significant features are extracted automatically from the raw sensor data by multi-branch SE-BiLSTM. The model can learn both short-term dependencies and long-term features in sequential data due to SqueezeNet and BiLSTM. The different temporal local dependencies are captured effectively by the proposed model, enhancing the feature extraction process. The hyperparameters of the BiLSTM network are optimized by the IFPOA. The model performance is analyzed using three benchmark datasets: MHEALTH, KU-HAR, and PAMPA2. The proposed model has achieved 99.98%, 99.76%, and 99.54% accuracies on MHEALTH, KU-HAR, and PAMPA2 datasets, respectively. The proposed model performs better than other approaches from the obtained experimental results. The suggested model delivers competitive results compared to state-of-the-art techniques, according to experimental results on four publicly accessible datasets.<\/jats:p>","DOI":"10.3390\/s23094319","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T04:30:47Z","timestamp":1682569847000},"page":"4319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors"],"prefix":"10.3390","volume":"23","author":[{"given":"Shaik","family":"Jameer","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, VIT AP University, Amaravati 522237, India"}]},{"given":"Hussain","family":"Syed","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, VIT AP University, Amaravati 522237, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nafea, O., Abdul, W., Muhammad, G., and Alsulaiman, M. (2021). Sensor-based human activity recognition with spatio-temporal deep learning. Sensors, 21.","DOI":"10.3390\/s21062141"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electronics, 10.","DOI":"10.3390\/electronics10030308"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107728","DOI":"10.1016\/j.asoc.2021.107728","article-title":"DanHAR: Dual attention network for multimodal human activity recognition using wearable sensors","volume":"111","author":"Gao","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.inffus.2021.11.006","article-title":"Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges","volume":"80","author":"Qiu","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tan, T.H., Wu, J.Y., Liu, S.H., and Gochoo, M. (2022). Human activity recognition using an ensemble learning algorithm with smartphone sensor data. Electronics, 11.","DOI":"10.3390\/electronics11030322"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116287","DOI":"10.1016\/j.eswa.2021.116287","article-title":"Human activity recognition using temporal convolutional neural network architecture","volume":"191","author":"Ledesma","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13041","DOI":"10.1109\/JIOT.2022.3140465","article-title":"AHAR: Adaptive CNN for energy-efficient human activity recognition in low-power edge devices","volume":"9","author":"Rashid","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51","DOI":"10.37965\/jait.2020.0051","article-title":"Human activity recognition and embedded application based on convolutional neural network","volume":"1","author":"Xu","year":"2021","journal-title":"J. Artif. Intell. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24471","DOI":"10.1109\/JSEN.2021.3113908","article-title":"A Lightweight Framework for Human Activity Recognition on Wearable Devices","volume":"21","author":"Coelho","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, Y.W., Joa, K.L., Jeong, H.Y., and Lee, S. (2021). Wearable IMU-based human activity recognition algorithm for clinical balance assessment using 1D-CNN and GRU ensemble model. Sensors, 21.","DOI":"10.3390\/s21227628"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17526","DOI":"10.1109\/JIOT.2022.3155560","article-title":"NoFED-Net: Non-Linear Fuzzy Ensemble of Deep Neural Networks for Human Activity Recognition","volume":"9","author":"Ghosal","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Boga, J. (Int. J. Pervasive Comput. Commun., 2022). Human activity recognition in WBAN using ensemble model, Int. J. Pervasive Comput. Commun., ahead of print.","DOI":"10.1108\/IJPCC-12-2021-0314"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). Lstm networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107338","DOI":"10.1016\/j.knosys.2021.107338","article-title":"A federated learning system with enhanced feature extraction for human activity recognition","volume":"229","author":"Xiao","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/LRA.2021.3059624","article-title":"Multi-gat: A graphical attention-based hierarchical multimodal representation learning approach for human activity recognition","volume":"6","author":"Islam","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s40860-021-00167-w","article-title":"Deep learning and model personalization in sensor-based human activity recognition","volume":"9","author":"Ferrari","year":"2022","journal-title":"J. Reliab. Intell. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, H., Hartmann, Y., and Schultz, T. (2022, January 10). A Practical Wearable Sensor-based Human Activity Recognition Research Pipeline. Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies, HEALTHINF, Vienna, Austria.","DOI":"10.5220\/0010937000003123"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neucom.2021.11.044","article-title":"DCNN based human activity recognition framework with depth vision guiding","volume":"486","author":"Qi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1109\/TIE.2022.3161812","article-title":"Multi-scale deep feature learning for human activity recognition using wearable sensors","volume":"70","author":"Tang","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Y., and Wang, L. (2022). Human Activity Recognition Based on Residual Network and BiLSTM. Sensors, 22.","DOI":"10.3390\/s22020635"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116764","DOI":"10.1016\/j.eswa.2022.116764","article-title":"Human activity recognition using wearable sensors by heterogeneous convolutional neural networks","volume":"198","author":"Han","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dirgov\u00e1 Lupt\u00e1kov\u00e1, I., Kubov\u010d\u00edk, M., and Posp\u00edchal, J. (2022). Wearable sensor-based human activity recognition with transformer model. Sensors, 22.","DOI":"10.20944\/preprints202202.0111.v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khan, I.U., Afzal, S., and Lee, J.W. (2022). Human activity recognition via hybrid deep learning based model. Sensors, 22.","DOI":"10.3390\/s22010323"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5889","DOI":"10.1109\/JSEN.2022.3149337","article-title":"Real-time human activity recognition using conditionally parametrized convolutions on mobile and wearable devices","volume":"22","author":"Cheng","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s00607-021-00928-8","article-title":"Multi-input CNN-GRU based human activity recognition using wearable sensors","volume":"103","author":"Dua","year":"2021","journal-title":"Computing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107671","DOI":"10.1016\/j.asoc.2021.107671","article-title":"Attention induced multi-head convolutional neural network for human activity recognition","volume":"110","author":"Khan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_27","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19361","DOI":"10.1007\/s11042-020-10435-1","article-title":"A time-efficient convolutional neural network model in human activity recognition","volume":"80","author":"Gholamrezaii","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e12743","DOI":"10.1111\/exsy.12743","article-title":"Deep ensemble learning approach for lower extremity activities recognition using wearable sensors","volume":"39","author":"Jain","year":"2022","journal-title":"Expert Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5369","DOI":"10.1007\/s11042-021-11885-x","article-title":"Inception inspired CNN-GRU hybrid network for human activity recognition","volume":"82","author":"Dua","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"22307","DOI":"10.1007\/s11042-021-11131-4","article-title":"Human activity recognition using deep transfer learning of cross position sensor based on vertical distribution of data","volume":"81","author":"Varshney","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s13735-022-00234-9","article-title":"Multi-sensor human activity recognition using CNN and GRU","volume":"11","author":"Nafea","year":"2022","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111445","DOI":"10.1016\/j.measurement.2022.111445","article-title":"Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning","volume":"199","author":"Dahou","year":"2022","journal-title":"Measurement"},{"key":"ref_34","first-page":"1","article-title":"RecurrentHAR: A Novel Transfer Learning-Based Deep Learning Model for Sequential, Complex, Concurrent, Interleaved, and Heterogeneous Type Human Activity Recognition","volume":"39","author":"Kumar","year":"2022","journal-title":"IETE Tech. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/JSEN.2021.3130761","article-title":"Human Activity Recognition Machine with an Anchor-Based Loss Function","volume":"22","author":"Jin","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, B., Wang, C., Huan, Z., Li, Z., Chen, Y., Gao, G., Li, H., Dong, C., and Liang, J. (2022). A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors. Sensors, 22.","DOI":"10.3390\/s22197446"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, L., and Liu, F. (2022). Multi-Branch Attention-Based Grouped Convolution Network for Human Activity Recognition Using Inertial Sensors. Electronics, 11.","DOI":"10.3390\/electronics11162526"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4490","DOI":"10.1109\/TSG.2020.2982351","article-title":"Deep learning-based real-time building occupancy detection using AMI data","volume":"11","author":"Feng","year":"2020","journal-title":"IEEE Trans. Smart Grid"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:24:20Z","timestamp":1760124260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094319"],"URL":"https:\/\/doi.org\/10.3390\/s23094319","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,27]]}}}