{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:27:22Z","timestamp":1780590442882,"version":"3.54.1"},"reference-count":119,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Multimedia University","award":["PostDoc(MMUI\/240029)"],"award-info":[{"award-number":["PostDoc(MMUI\/240029)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Ambient Assisted Living (AAL) leverages technology to support the elderly and individuals with disabilities. A key challenge in these systems is efficient Human Activity Recognition (HAR). However, no study has systematically compared single-view (SV) and multi-view (MV) Human Activity Recognition approaches. This review addresses this gap by analyzing the evolution from single-view to multi-view recognition systems, covering benchmark datasets, feature extraction methods, and classification techniques. We examine how activity recognition systems have transitioned to multi-view architectures using advanced deep learning models optimized for Ambient Assisted Living, thereby improving accuracy and robustness. Furthermore, we explore a wide range of machine learning and deep learning models\u2014including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Graph Convolutional Networks (GCNs)\u2014along with lightweight transfer learning methods suitable for environments with limited computational resources. Key challenges such as data remediation, privacy, and generalization are discussed, alongside potential solutions such as sensor fusion and advanced learning strategies. This study offers comprehensive insights into recent advancements and future directions, guiding the development of intelligent, efficient, and privacy-compliant Human Activity Recognition systems for Ambient Assisted Living applications.<\/jats:p>","DOI":"10.3390\/jimaging11060182","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T03:57:34Z","timestamp":1749009454000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-2348","authenticated-orcid":false,"given":"Fahmid","family":"Al Farid","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahsanul","family":"Bari","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abu Saleh Musa","family":"Miah","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology (BAUST), Saidpur 5311, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4939-0631","authenticated-orcid":false,"given":"Sarina","family":"Mansor","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3403-4095","authenticated-orcid":false,"given":"Jia","family":"Uddin","sequence":"additional","affiliation":[{"name":"AI and Big Data Department, Woosong University, Daejeon 34606, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-7428","authenticated-orcid":false,"given":"S. Prabha","family":"Kumaresan","sequence":"additional","affiliation":[{"name":"Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhou, D., Wang, J., Wang, S., Guan, Y., He, X., and Ding, E. (2021, January 17). Learning multi-granular spatio-temporal graph network for skeleton-based action recognition. Proceedings of the 29th ACM International Conference on Multimedia, MM \u201921: ACM Multimedia Conference, Virtual Event, China.","DOI":"10.1145\/3474085.3475574"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Miah, A.S.M., Islam, M.R., and Molla, M.K.I. (2017, January 22\u201324). Motor imagery classification using subband tangent space mapping. Proceedings of the 2017 20th International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCITECHN.2017.8281828"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Miah, A.S.M., Islam, M.R., and Molla, M.K.I. (2019, January 11\u201312). EEG classification for MI-BCI using CSP with averaging covariance matrices: An experimental study. Proceedings of the 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh.","DOI":"10.1109\/IC4ME247184.2019.9036591"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tusher, M.M.R., Farid, F.A., Kafi, H.M., Miah, A.S.M., Rinky, S.R., Islam, M., Rahim, M.A., Mansor, S., and Karim, H.A. (2024, January 16\u201322). BanTrafficNet: Bangladeshi Traffic Sign Recognition Using a Lightweight Deep Learning Approach. Proceedings of the Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.21203\/rs.3.rs-4216970\/v1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zobaed, T., Ahmed, S.R.A., Miah, A.S.M., Binta, S.M., Ahmed, M.R.A., and Rashid, M. (2020). Real time sleep onset detection from single channel EEG signal using block sample entropy. Iop Conf. Ser. Mater. Sci. Eng., 928.","DOI":"10.1088\/1757-899X\/928\/3\/032021"},{"key":"ref_6","first-page":"9","article-title":"Potential Disease Detection Using Naive Bayes and Random Forest Approach","volume":"2","author":"Ali","year":"2022","journal-title":"BAUST J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hossain, M.M., Chowdhury, Z.R., Akib, S.M.R.H., Ahmed, M.S., Hossain, M.M., and Miah, A.S.M. (2023, January 13\u201315). Crime Text Classification and Drug Modeling from Bengali News Articles: A Transformer Network-Based Deep Learning Approach. Proceedings of the 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT60459.2023.10441195"},{"key":"ref_8","first-page":"1689","article-title":"An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis","volume":"140","author":"Rahim","year":"2024","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Miah, A.S.M., Ahmed, S.R.A., Ahmed, M.R., Bayat, O., Duru, A.D., and Molla, M.K.I. (2019, January 24\u201326). Motor-Imagery BCI task classification using riemannian geometry and averaging with mean absolute deviation. Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey.","DOI":"10.1109\/EBBT.2019.8741603"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kibria, K.A., Noman, A.S., Hossain, M.A., Bulbul, M.S.I., Rashid, M.M., and Miah, A.S.M. (2020, January 5\u20137). Creation of a Cost-Efficient and Effective Personal Assistant Robot using Arduino Machine Learning Algorithm. Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh.","DOI":"10.1109\/TENSYMP50017.2020.9230773"},{"key":"ref_11","first-page":"10","article-title":"Exploring Bangladesh\u2019s Soil Moisture Dynamics via Multispectral Remote Sensing Satellite Image","volume":"4","author":"Hossain","year":"2023","journal-title":"Eur. J. Environ. Earth Sci."},{"key":"ref_12","first-page":"56","article-title":"A Comparative Review of Detecting Alzheimer\u2019s Disease Using Various Methodologies","volume":"1","author":"Rahman","year":"2020","journal-title":"BAUST J."},{"key":"ref_13","first-page":"2633","article-title":"Development of a Lightweight Model for Handwritten Dataset Recognition: Bangladeshi City Names in Bangla Script","volume":"80","author":"Tusher","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s11416-024-00529-x","article-title":"Next gen cybersecurity paradigm towards artificial general intelligence: Russian market challenges and future global technological trends","volume":"20","author":"Pleshakova","year":"2024","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11416-023-00500-2","article-title":"Machine learning methods for speech emotion recognition on telecommunication systems","volume":"20","author":"Osipov","year":"2024","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Obinata, Y., and Yamamoto, T. (2021, January 10\u201315). Temporal Extension Module for Skeleton-Based Action Recognition. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412113"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_18","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Chen, J., Wang, Y., Cao, Z., Zhou, J.T., and Bai, X. (2018). Action Recognition for Depth Video using Multi-view Dynamic Images. arXiv.","DOI":"10.1016\/j.ins.2018.12.050"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019). Deep High-Resolution Representation Learning for Human Pose Estimation. arXiv.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Luvizon, D.C., Picard, D., and Tabia, H. (2018). 2D\/3D Pose Estimation and Action Recognition using Multitask Deep Learning. arXiv.","DOI":"10.1109\/CVPR.2018.00539"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","article-title":"NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding","volume":"42","author":"Liu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","unstructured":"Yang, D., Li, M.M., Fu, H., Fan, J., Zhang, Z., and Leung, H. (2020). Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guerra, B.M.V., Torti, E., Marenzi, E., Schmid, M., Ramat, S., Leporati, F., and Danese, G. (2023). Ambient assisted living for frail people through human activity recognition: State-of-the-art, challenges and future directions. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1256682"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhao, Y., Chen, K., Lin, D., and Dai, B. (2021). Revisiting Skeleton-based Action Recognition. arXiv.","DOI":"10.1109\/CVPR52688.2022.00298"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhang, L., and Shang, H. (2022). A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition. Sensors, 22.","DOI":"10.3390\/s22155482"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Action, S.G.H. (2022). Skeleton Graph-Neural-Network-Based Human Action. Sensors, 22.","DOI":"10.3390\/s22062091"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a new benchmarked dataset for activity monitoring. Proceedings of the 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_29","unstructured":"Blunck, H., Bhattacharya, S., Prentow, T., Kjrgaard, M., and Dey, A. (2025, March 20). Heterogeneity Activity Recognition. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/344\/heterogeneity+activity+recognition."},{"key":"ref_30","unstructured":"Banos, O., Garcia, R., and Saez, A. (2025, March 20). MHEALTH. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/319\/mhealth+dataset."},{"key":"ref_31","unstructured":"Reyes-Ortiz, J., Anguita, D., Ghio, A., Oneto, L., and Parra, X. (2025, March 20). Human Activity Recognition Using Smartphones. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/240\/human+activity+recognition+using+smartphones."},{"key":"ref_32","unstructured":"Roggen, D., Calatroni, A., Nguyen-Dinh, L., Chavarriaga, R., and Sagha, H. (2025, March 20). OPPORTUNITY Activity Recognition. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/226\/opportunity+activity+recognition."},{"key":"ref_33","unstructured":"Weiss, G. (2025, March 20). WISDM Smartphone and Smartwatch Activity and Biometrics Dataset. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/507\/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2017). UniMiB SHAR: A new dataset for human activity recognition using acceleration data from smartphones. Appl. Sci., 7.","DOI":"10.20944\/preprints201706.0033.v1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"R\u00f6cker, C., O\u2019Donoghue, J., Ziefle, M., Helfert, M., and Molloy, W. (2017). Human Daily Activity and Fall Recognition Using a Smartphone\u2019s Acceleration Sensor. Information and Communication Technologies for Ageing Well and e-Health, Springer.","DOI":"10.1007\/978-3-319-62704-5"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ba\u00f1os, O., Garc\u00eda, R., Terriza, J.A.H., Damas, M., Pomares, H., Rojas, I., Saez, A., and Villalonga, C. (2014, January 2\u20135). mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. Proceedings of the International Workshop on Ambient Assisted Living and Home Care, Belfast, UK.","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting complex activity datasets in highly rich networked sensor environments. Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_39","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A Public Domain Dataset for Human Activity Recognition using Smartphones. Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a New Benchmarked Dataset for Activity Monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_41","unstructured":"Barshan, B., and Altun, K. (2025, March 20). Daily and Sports Activities. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/256\/daily+and+sports+activities."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, NSW, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_43","unstructured":"Oktay, B., Sab\u0131r, M., and Tuameh, M. (2025, May 24). Fitness Exercise Pose Classification. Kaggle. Available online: https:\/\/kaggle.com\/competitions\/fitness-pose-classification."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., and Kehtarnavaz, N. (2015, January 27\u201330). UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"ref_45","unstructured":"Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M. (2020). Tudataset: A collection of benchmark datasets for learning with graphs. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Raja Sekaran, S., Pang, Y.H., You, L.Z., and Yin, O.S. (2024). A hybrid TCN-GRU model for classifying human activities using smartphone inertial signals. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0304655"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. Proceedings of the ACM International Conference on Ubiquitous Computing (Ubicomp) Workshop on Situation, Activity and Goal Awareness (SAGAware), Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"143","DOI":"10.5220\/0005792401430151","article-title":"The mobiact dataset: Recognition of activities of daily living using smartphones","volume":"Volume 2","author":"Vavoulas","year":"2016","journal-title":"Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R.G., Cavallaro, A., and Haddadi, H. (2019, January 15\u201318). Mobile Sensor Data Anonymization. Proceedings of the International Conference on Internet of Things Design and Implementation\u2014IoTDI \u201919, Montreal, QC, Canada.","DOI":"10.1145\/3302505.3310068"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pati\u00f1o-Saucedo, J.A., Ariza-Colpas, P.P., Butt-Aziz, S., Pi\u00f1eres-Melo, M.A., L\u00f3pez-Ruiz, J.L., Morales-Ortega, R.C., and De-la-hoz Franco, E. (2022). Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191912272"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2345770.2345781","article-title":"Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach","volume":"11","author":"Zappi","year":"2012","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"ref_52","unstructured":"Yang, Z., Zhang, Y., Zhang, G., and Zheng, Y. (2020). Widar 3.0: WiFi-based activity recognition dataset. IEEE Dataport."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","article-title":"Transition-aware human activity recognition using smartphones","volume":"171","author":"Oneto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Qi, W., Su, H., Yang, C., Ferrigno, G., De Momi, E., and Aliverti, A. (2019). A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone. Sensors, 19.","DOI":"10.3390\/s19173731"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Plizzari, C., Cannici, M., and Matteucci, M. (2021). Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks. arXiv.","DOI":"10.1016\/j.cviu.2021.103219"},{"key":"ref_56","unstructured":"Jin, S., Xu, L., Xu, J., Wang, C., Liu, W., Qian, C., Ouyang, W., and Luo, P. (2007). Whole-Body Human Pose Estimation in the Wild. arXiv."},{"key":"ref_57","unstructured":"Qin, Z., Liu, Y., Ji, P., Kim, D., Wang, L., McKay, B., Anwar, S., and Gedeon, T. (2022). Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mitra, S., and Kanungoe, P. (2023, January 11\u201313). Smartphone based Human Activity Recognition using CNNs and Autoencoder Features. Proceedings of the 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI56765.2023.10126051"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Badawi, A.A., Al-Kabbany, A., and Shaban, H. (2018, January 3\u20136). Multimodal Human Activity Recognition From Wearable Inertial Sensors Using Machine Learning. Proceedings of the 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia.","DOI":"10.1109\/IECBES.2018.8626737"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17610","DOI":"10.1109\/ACCESS.2021.3051899","article-title":"Heterogeneous Sensor Data Fusion for Human Falling Detection","volume":"9","author":"Pan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kim, C., and Lee, W. (2023). Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data. Appl. Sci., 13.","DOI":"10.3390\/app13084961"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sharma, V., Gupta, M., Pandey, A.K., Mishra, D., and Kumar, A. (2022). A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets. Appl. Artif. Intell., 36.","DOI":"10.1080\/08839514.2022.2093705"},{"key":"ref_63","unstructured":"Peng, W., Hong, X., Chen, H., and Zhao, G. (2020, January 7\u201312). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI 2020\u201434th AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Albahar, M. (2023). A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities. Agriculture, 13.","DOI":"10.3390\/agriculture13030540"},{"key":"ref_65","unstructured":"Bai, Y., Tao, Z., Wang, L., Li, S., Yin, Y., and Fu, Y. (2009). Collaborative Attention Mechanism for Multi-View Action Recognition. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Madokoro, H., Nix, S., Woo, H., and Sato, K. (2021). A mini-survey and feasibility study of deep-learning-based human activity recognition from slight feature signals obtained using privacy-aware environmental sensors. Appl. Sci., 11.","DOI":"10.3390\/app112411807"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.inffus.2017.06.004","article-title":"Multi-view stacking for activity recognition with sound and accelerometer data","volume":"40","author":"Brena","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/JSEN.2021.3069927","article-title":"Human Activity Recognition with Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review","volume":"21","author":"Ramanujam","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Dua, N., Singh, S.N., Challa, S.K., Semwal, V.B., and Kumar, M.L.S. (2022, January 8\u20139). A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data. Proceedings of the Communications in Computer and Information Science, Munster, Ireland.","DOI":"10.1007\/978-3-031-24352-3_5"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","article-title":"Real-time human activity recognition from accelerometer data using convolutional neural networks","volume":"62","author":"Ignatov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1109\/TNNLS.2019.2927224","article-title":"A semisupervised recurrent convolutional attention model for human activity recognition","volume":"31","author":"Chen","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"10815","DOI":"10.1007\/s11042-023-15830-y","article-title":"Human activity recognition from multiple sensors data using deep CNNs","volume":"83","author":"Kaya","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s12530-022-09480-y","article-title":"A human activity recognition method using wearable sensors based on convtransformer model","volume":"14","author":"Zhang","year":"2023","journal-title":"Evol. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Murad, A., and Pyun, J.Y. (2017). Deep recurrent neural networks for human activity recognition. Sensors, 17.","DOI":"10.3390\/s17112556"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Nooruddin, S., and Karray, F. (2022, January 9\u201312). Multimodal Human Activity Recognition for Smart Healthcare Applications. Proceedings of the Conference Proceedings\u2014IEEE International Conference on Systems, Man and Cybernetics, Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945513"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Alawneh, L., Mohsen, B., Al-Zinati, M., Shatnawi, A., and Al-Ayyoub, M. (2020, January 23\u201327). A comparison of unidirectional and Bidirectional LSTM networks for human activity recognition. Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA.","DOI":"10.1109\/PerComWorkshops48775.2020.9156264"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Gupta, S. (2021). Deep learning based human activity recognition (HAR) using wearable sensor data. Int. J. Inf. Manag. Data Insights, 1.","DOI":"10.1016\/j.jjimei.2021.100046"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.aej.2024.01.030","article-title":"A multi-channel hybrid deep learning framework for multi-sensor fusion enabled human activity recognition","volume":"91","author":"Zhang","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4628","DOI":"10.1109\/JIOT.2020.3026732","article-title":"Data augmentation and dense-LSTM for human activity recognition using Wi-Fi signal","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/JSEN.2017.2782492","article-title":"Human activity classification in smartphones using accelerometer and gyroscope sensors","volume":"18","author":"Jain","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_82","first-page":"1","article-title":"A novel multichannel dilated convolution neural network for human activity recognition","volume":"2020","author":"Lin","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"21465","DOI":"10.1007\/s11042-021-10687-5","article-title":"Automatic human posture estimation for sport activity recognition with robust body parts detection and entropy Markov model","volume":"80","author":"Nadeem","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Kavuncuo\u011flu, E., Uzunhisarc\u0131kl\u0131, E., Barshan, B., and \u00d6zdemir, A.T. (2022). Investigating the performance of wearable motion sensors on recognizing falls and daily activities via machine learning. Digit. Signal Process., 126.","DOI":"10.1016\/j.dsp.2021.103365"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"66797","DOI":"10.1109\/ACCESS.2022.3185112","article-title":"A multichannel CNN-GRU model for human activity recognition","volume":"10","author":"Lu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Kim, Y.W., Cho, W.H., Kim, K.S., and Lee, S. (2022). Oversampling technique-based data augmentation and 1D-CNN and bidirectional GRU ensemble model for human activity recognition. J. Mech. Med. Biol., 22.","DOI":"10.1142\/S0219519422400486"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"5165","DOI":"10.1007\/s00521-022-07911-0","article-title":"Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm","volume":"35","author":"Sarkar","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"33401","DOI":"10.1007\/s11042-023-14733-2","article-title":"Gait reference trajectory generation at different walking speeds using LSTM and CNN","volume":"82","author":"Semwal","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"12863","DOI":"10.1109\/JSEN.2024.3371462","article-title":"Revisiting Large-Kernel CNN Design via Structural Re-Parameterization for Sensor-Based Human Activity Recognition","volume":"24","author":"Yao","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wei, X., and Wang, Z. (2024). TCN-Attention-HAR: Human activity recognition based on attention mechanism time convolutional network. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-57912-3"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"El-Adawi, E., Essa, E., Handosa, M., and Elmougy, S. (2024). Wireless body area sensor networks based human activity recognition using deep learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-53069-1"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Ye, X., and Wang, K.I.K. (2024). Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition. arXiv.","DOI":"10.1016\/j.patcog.2024.110811"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Xu, L. (2024). Multi-STMT: Multi-level network for human activity recognition based on wearable sensors. IEEE Trans. Instrum. Meas., 73.","DOI":"10.1109\/TIM.2024.3365155"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Saha, U., Saha, S., Kabir, M.T., Fattah, S.A., and Saquib, M. (2024). Decoding human activities: Analyzing wearable accelerometer and gyroscope data for activity recognition. IEEE Sens. Lett., 8.","DOI":"10.1109\/LSENS.2024.3423340"},{"key":"ref_95","unstructured":"Shahabian Alashti, M.R., Bamorovat Abadi, M., Holthaus, P., Menon, C., and Amirabdollahian, F. (2023, January 24\u201328). Lightweight human activity recognition for ambient assisted living. Proceedings of the ACHI 2023: The Sixteenth International Conference on Advances in Computer-Human Interactions, Venice, Italy."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.1109\/JBHI.2020.3006145","article-title":"An attention based CNN-LSTM approach for sleep-wake detection with heterogeneous sensors","volume":"25","author":"Chen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Essa, E., and Abdelmaksoud, I.R. (2023). Temporal-channel convolution with self-attention network for human activity recognition using wearable sensors. Knowl.-Based Syst., 278.","DOI":"10.1016\/j.knosys.2023.110867"},{"key":"ref_98","unstructured":"Kim, H., and Lee, D. (2024). CLAN: A Contrastive Learning based Novelty Detection Framework for Human Activity Recognition. arXiv."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-981-16-0575-8_1","article-title":"Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark","volume":"Volume 1370","author":"Mostafa","year":"2021","journal-title":"Communications in Computer and Information Science"},{"key":"ref_100","unstructured":"Madsen, H. (2007). Time Series Analysis, Chapman and Hall\/CRC."},{"key":"ref_101","unstructured":"Gori, L.R., Tapaswi, M., Liao, R., Jia, J., Urtasun, R., and Fidler, S. (2017, January 22\u201329). Situation recognition with graph neural networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., K\u00f6ping, L., and Grzegorzek, M. (2018). Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors, 18.","DOI":"10.3390\/s18020679"},{"key":"ref_103","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., and Lu, H. (2019, January 15\u201320). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref_106","unstructured":"Shiraki, K., Hirakawa, T., Yamashita, T., and Fujiyoshi, H. (December, January 30). Spatial temporal attention graph convolutional networks with mechanics-stream for skeleton-based action recognition. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","article-title":"Skeleton-based action recognition with multi-stream adaptive graph convolutional networks","volume":"29","author":"Shi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Huang, J., Huang, Z., Xiang, X., Gong, X., and Zhang, B. (2020, January 1\u20135). Long-short graph memory network for skeleton-based action recognition. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093598"},{"key":"ref_109","unstructured":"Thakkar, K., and Narayanan, P. (2018). Part-based graph convolutional network for action recognition. arXiv."},{"key":"ref_110","unstructured":"Howard, A.G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_111","unstructured":"Iandola, F.N. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_112","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_113","unstructured":"Han, S., Pool, J., Tran, J., and Dally, W. (2015). Learning both weights and connections for efficient neural network. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Awais, M., Raza, M., Ali, K., Ali, Z., Irfan, M., Chughtai, O., Khan, I., Kim, S., and Ur Rehman, M. (2019). An internet of things based bed-egress alerting paradigm using wearable sensors in elderly care environment. Sensors, 19.","DOI":"10.3390\/s19112498"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/JSEN.2015.2487358","article-title":"A real-time human action recognition system using depth and inertial sensor fusion","volume":"16","author":"Chen","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_116","unstructured":"Hinton, G. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_117","unstructured":"Polino, A., Pascanu, R., and Alistarh, D. (2018). Model compression via distillation and quantization. arXiv."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3380980","article-title":"DeepMV: Multi-view deep learning for device-free human activity recognition","volume":"4","author":"Xue","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Li, Q., Xu, L., and Yang, X. (2022). 2D multi-person pose estimation combined with face detection. Int. J. Pattern Recognit. Artif. 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