{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T18:07:00Z","timestamp":1780942020823,"version":"3.54.1"},"reference-count":122,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MUR-Italian Ministry for University and Research","award":["ARS01_00345"],"award-info":[{"award-number":["ARS01_00345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.<\/jats:p>","DOI":"10.3390\/s23115281","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T02:16:48Z","timestamp":1685672208000},"page":"5281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-3721","authenticated-orcid":false,"given":"Giovanni","family":"Diraco","sequence":"first","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3374-2433","authenticated-orcid":false,"given":"Gabriele","family":"Rescio","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1312-4593","authenticated-orcid":false,"given":"Pietro","family":"Siciliano","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-3313","authenticated-orcid":false,"given":"Alessandro","family":"Leone","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121427","DOI":"10.1016\/j.techfore.2021.121427","article-title":"The structural model of indicators for evaluating the quality of urban smart living","volume":"176","author":"Shami","year":"2022","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_2","first-page":"1","article-title":"Overview of spintronic sensors with internet of things for smart living","volume":"55","author":"Liu","year":"2019","journal-title":"IEEE Trans. Magn."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yasirandi, R., Lander, A., Sakinah, H.R., and Insan, I.M. (2020, January 24\u201326). IoT products adoption for smart living in Indonesia: Technology challenges and prospects. Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICoICT49345.2020.9166200"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/13511610.2012.660323","article-title":"Smartness and European urban performance: Assessing the local impacts of smart urban attributes","volume":"25","author":"Caragliu","year":"2012","journal-title":"Innov. Eur. J. Soc. Sci. Res."},{"key":"ref_5","unstructured":"Dameri, R.P., and Ricciardi, F. (2017). Smart City Networks: Through Internet Things, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/17535069.2010.524420","article-title":"The role of rankings in growing city competition","volume":"3","author":"Giffinger","year":"2010","journal-title":"Urban Res. Pract."},{"key":"ref_7","first-page":"367","article-title":"Technical Analysis of Security Management in Terms of Crowd Energy and Smart Living","volume":"16","author":"Khan","year":"2018","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102312","DOI":"10.1016\/j.habitatint.2021.102312","article-title":"A critical review of the smart city in relation to citizen adoption towards sustainable smart living","volume":"108","author":"Han","year":"2021","journal-title":"Habitat Int."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., and Scholl, H.J. (2012, January 4\u20137). Understanding smart cities: An integrative framework. Proceedings of the 2012 45th Hawaii International Conference on System Sciences, Maui, HI, USA.","DOI":"10.1109\/HICSS.2012.615"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zin, T.T., Htet, Y., Akagi, Y., Tamura, H., Kondo, K., Araki, S., and Chosa, E. (2021). Real-time action recognition system for elderly people using stereo depth camera. Sensors, 21.","DOI":"10.3390\/s21175895"},{"key":"ref_11","unstructured":"Rathod, V., Katragadda, R., Ghanekar, S., Raj, S., Kollipara, P., Anitha Rani, I., and Vadivel, A. (2019, January 29\u201330). Smart surveillance and real-time human action recognition using OpenPose. Proceedings of the ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications, Hyderabad, India."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1007\/s10489-019-01603-4","article-title":"Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals","volume":"50","author":"Lu","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sowmyayani, S., and Rani, P.A.J. (2022). STHARNet: Spatio-temporal human action recognition network in content based video retrieval. Multimed. Tools Appl., 1\u201316.","DOI":"10.1007\/s11042-022-14056-8"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rodomagoulakis, I., Kardaris, N., Pitsikalis, V., Mavroudi, E., Katsamanis, A., Tsiami, A., and Maragos, P. (2016, January 20\u201325). Multimodal human action recognition in assistive human-robot interaction. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472168"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isatra.2022.10.034","article-title":"Ambient intelligence-based multimodal human action recognition for autonomous systems","volume":"132","author":"Jain","year":"2023","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-time human pose recognition in parts from single depth images. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_17","unstructured":"Keestra, M. (2015). Transdisciplinarity in Philosophy and Science: Approaches, Problems, Prospects = Transdistsiplinarnost v Filosofii i Nauke: Podkhody, Problemy, Perspektivy, Navigator."},{"key":"ref_18","unstructured":"Ricoeur, P. (1992). Oneself as Another, University of Chicago Press."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1146\/annurev.psych.57.102904.190152","article-title":"Perception of human motion","volume":"58","author":"Blake","year":"2007","journal-title":"Annu. Rev. Psychol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.1016\/j.proeng.2011.08.532","article-title":"Human action recognition based on template matching","volume":"15","author":"Li","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thi, T.H., Zhang, J., Cheng, L., Wang, L., and Satoh, S. (September, January 29). Human action recognition and localization in video using structured learning of local space-time features. Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, MA, USA.","DOI":"10.1109\/AVSS.2010.76"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.cviu.2010.10.002","article-title":"A survey of vision-based methods for action representation, segmentation and recognition","volume":"115","author":"Weinland","year":"2011","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","first-page":"1","article-title":"A survey on deep learning for human activity recognition","volume":"54","author":"Gu","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1038\/s41597-022-01573-2","article-title":"OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors","volume":"9","author":"Bocus","year":"2022","journal-title":"Sci. Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113557","DOI":"10.1016\/j.sna.2022.113557","article-title":"Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms","volume":"341","author":"Vidya","year":"2022","journal-title":"Sens. Actuators A Phys."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ma, S., Sigal, L., and Sclaroff, S. (2016, January 27\u201330). Learning activity progression in lstms for activity detection and early detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.214"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kong, Y., Kit, D., and Fu, Y. (2014, January 6\u201312). A discriminative model with multiple temporal scales for action prediction. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_39"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119254","DOI":"10.1016\/j.eswa.2022.119254","article-title":"Vehicles driving behavior recognition based on transfer learning","volume":"213","author":"Chen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.ins.2022.12.014","article-title":"Analysis of multimodal data fusion from an information theory perspective","volume":"623","author":"Dai","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1049\/iet-cvi.2018.5103","article-title":"Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy","volume":"12","author":"Xian","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4145","DOI":"10.1007\/s00521-022-07937-4","article-title":"Toward human activity recognition: A survey","volume":"35","author":"Saleem","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arshad, M.H., Bilal, M., and Gani, A. (2022). Human Activity Recognition: Review, Taxonomy and Open Challenges. Sensors, 22.","DOI":"10.3390\/s22176463"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18289","DOI":"10.1007\/s00521-022-07665-9","article-title":"A review of machine learning-based human activity recognition for diverse applications","volume":"34","author":"Kulsoom","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_34","first-page":"3200","article-title":"Human action recognition from various data modalities: A review","volume":"45","author":"Sun","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Najeh, H., Lohr, C., and Leduc, B. (2022, January 25\u201327). Towards supervised real-time human activity recognition on embedded equipment. Proceedings of the 2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn), Cosenza, Italy.","DOI":"10.1109\/MetroLivEnv54405.2022.9826937"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bian, S., Liu, M., Zhou, B., and Lukowicz, P. (2022). The state-of-the-art sensing techniques in human activity recognition: A survey. Sensors, 22.","DOI":"10.3390\/s22124596"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1007\/s10462-021-10116-x","article-title":"Human activity recognition in artificial intelligence framework: A narrative review","volume":"55","author":"Gupta","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109363","DOI":"10.1016\/j.asoc.2022.109363","article-title":"A survey on unsupervised learning for wearable sensor-based activity recognition","volume":"127","author":"Ige","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"973","DOI":"10.26599\/TST.2021.9010068","article-title":"A Survey of Human Action Recognition and Posture Prediction","volume":"27","author":"Ma","year":"2022","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1007\/s11831-021-09681-9","article-title":"Progress of human action recognition research in the last ten years: A comprehensive survey","volume":"29","author":"Singh","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s11263-022-01594-9","article-title":"Human action recognition and prediction: A survey","volume":"130","author":"Kong","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2017). Unimib shar: A dataset for human activity recognition using acceleration data from smartphones. Appl. Sci., 7.","DOI":"10.20944\/preprints201706.0033.v1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MIS.2010.112","article-title":"Learning setting-generalized activity models for smart spaces","volume":"27","author":"Cook","year":"2012","journal-title":"IEEE Intell. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MC.2012.328","article-title":"CASAS: A smart home in a box","volume":"46","author":"Cook","year":"2012","journal-title":"Computer"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"480","DOI":"10.3414\/ME0592","article-title":"Assessing the quality of activities in a smart environment","volume":"48","author":"Cook","year":"2009","journal-title":"Methods Inf. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s12652-009-0007-1","article-title":"Recognizing independent and joint activities among multiple residents in smart environments","volume":"1","author":"Singla","year":"2010","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/01969720903584183","article-title":"Detection of social interaction in smart spaces","volume":"41","author":"Cook","year":"2010","journal-title":"Cybern. Syst. Int. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","article-title":"Smartphone and smartwatch-based biometrics using activities of daily living","volume":"7","author":"Weiss","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MPRV.2017.3971131","article-title":"Recognizing detailed human context in the wild from smartphones and smartwatches","volume":"16","author":"Vaizman","year":"2017","journal-title":"IEEE Pervasive Comput."},{"key":"ref_52","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 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPRV.2008.40","article-title":"Wearable activity tracking in car manufacturing","volume":"7","author":"Stiefmeier","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Villase\u00f1or, L., Ponce, H., Brieva, J., Moya-Albor, E., N\u00fa\u00f1ez-Mart\u00ednez, J., and Pe\u00f1afort-Asturiano, C. (2019). UP-fall detection dataset: A multimodal approach. Sensors, 19.","DOI":"10.3390\/s19091988"},{"key":"ref_55","unstructured":"Kelly, J. (2023, February 06). UK Domestic Appliance-Level Electricity (UK-DALE) Dataset. Available online: https:\/\/jack-kelly.com\/data\/."},{"key":"ref_56","unstructured":"Arrotta, L., Bettini, C., and Civitarese, G. (2021, January 8\u201311). The marble dataset: Multi-inhabitant activities of daily living combining wearable and environmental sensors data. Proceedings of the Mobile and Ubiquitous Systems: Computing, Networking and Services: 18th EAI International Conference, MobiQuitous 2021, Virtual."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Schuldt, C., Laptev, I., and Caputo, B. (2004, January 26). Recognizing human actions: A local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Blank, M., Gorelick, L., Shechtman, E., Irani, M., and Basri, R. (2005, January 17\u201321). Actions as space-time shapes. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905), Beijing, China.","DOI":"10.1109\/ICCV.2005.28"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rodriguez, M.D., Ahmed, J., and Shah, M. (2008, January 23\u201328). Action mach a spatio-temporal maximum average correlation height filter for action recognition. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587727"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Niemann, F., Reining, C., Moya Rueda, F., Nair, N.R., Steffens, J.A., Fink, G.A., and Ten Hompel, M. (2020). Lara: Creating a dataset for human activity recognition in logistics using semantic attributes. Sensors, 20.","DOI":"10.3390\/s20154083"},{"key":"ref_62","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_63","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J. (2016). Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_64","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_65","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_66","doi-asserted-by":"crossref","unstructured":"Ramos, R.G., Domingo, J.D., Zalama, E., G\u00f3mez-Garc\u00eda-Bermejo, J., and L\u00f3pez, J. (2022). SDHAR-HOME: A sensor dataset for human activity recognition at home. Sensors, 22.","DOI":"10.3390\/s22218109"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Arrotta, L., Bettini, C., and Civitarese, G. (2022). MICAR: Multi-inhabitant context-aware activity recognition in home environments. Distrib. Parallel Databases, 1\u201332.","DOI":"10.1007\/s10619-022-07403-z"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s44147-022-00098-0","article-title":"A novel human activity recognition architecture: Using residual inception ConvLSTM layer","volume":"69","author":"Khater","year":"2022","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Mohtadifar, M., Cheffena, M., and Pourafzal, A. (2022). Acoustic-and Radio-Frequency-Based Human Activity Recognition. Sensors, 22.","DOI":"10.3390\/s22093125"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Delaine, F., and Faraut, G. (2022). Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition. Sensors, 22.","DOI":"10.3390\/s22072439"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3517224","article-title":"Dexar: Deep explainable sensor-based activity recognition in smart-home environments","volume":"6","author":"Arrotta","year":"2022","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1007\/s12652-016-0437-5","article-title":"Semantic event fusion of computer vision and ambient sensor data for activity recognition to support dementia care","volume":"11","author":"Stavropoulos","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_73","first-page":"3221","article-title":"Smart devices based multisensory approach for complex human activity recognition","volume":"70","author":"Hanif","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.procs.2022.03.007","article-title":"Hand Gestures Identification for Fine-Grained Human Activity Recognition in Smart Homes","volume":"201","author":"Roberge","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Syed, A.S., Sierra-Sosa, D., Kumar, A., and Elmaghraby, A. (2021). A hierarchical approach to activity recognition and fall detection using wavelets and adaptive pooling. Sensors, 21.","DOI":"10.3390\/s21196653"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Achirei, S.D., Heghea, M.C., Lupu, R.G., and Manta, V.I. (2022). Human Activity Recognition for Assisted Living Based on Scene Understanding. Appl. Sci., 12.","DOI":"10.3390\/app122110743"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"107774","DOI":"10.1016\/j.measurement.2020.107774","article-title":"Internet of things sensors assisted physical activity recognition and health monitoring of college students","volume":"159","author":"Zhong","year":"2020","journal-title":"Measurement"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, A., Zhao, S., Keh, H.C., Chen, G., and Roy, D.S. (2021). Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition. Sensors, 21.","DOI":"10.3390\/s21216962"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Fan, C., and Gao, F. (2021). Enhanced human activity recognition using wearable sensors via a hybrid feature selection method. Sensors, 21.","DOI":"10.3390\/s21196434"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"33527","DOI":"10.1007\/s11042-021-11105-6","article-title":"Fusion of smartphone sensor data for classification of daily user activities","volume":"80","author":"Ozcelik","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"164453","DOI":"10.1109\/ACCESS.2020.3022287","article-title":"WiWeHAR: Multimodal human activity recognition using Wi-Fi and wearable sensing modalities","volume":"8","author":"Muaaz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_82","first-page":"644","article-title":"Using wearable sensors for human activity recognition in logistics: A comparison of different feature sets and machine learning algorithms","volume":"11","author":"Syed","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, J., Huang, X., Jiang, H., and Miao, X. (2021). Low-cost and device-free human activity recognition based on hierarchical learning model. Sensors, 21.","DOI":"10.3390\/s21072359"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4598460","DOI":"10.1155\/2022\/4598460","article-title":"Device-Free Human Activity Recognition Based on Dual-Channel Transformer Using WiFi Signals","volume":"2022","author":"Gu","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wu, Y.H., Chen, Y., Shirmohammadi, S., and Hsu, C.H. (2022, January 10). AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars. Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management, Lisboa, Portugal.","DOI":"10.1145\/3552484.3555753"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230977","article-title":"Radar Point Clouds Processing for Human Activity Classification using Convolutional Multilinear Subspace Learning","volume":"60","author":"Qiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"105107","DOI":"10.1088\/1361-6501\/ac7779","article-title":"Application of multi-angle millimeter-wave radar detection in human motion behavior and micro-action recognition","volume":"33","author":"Zhang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Z., Zhao, Z., Jin, Y., Yin, J., Huang, S.L., and Wang, J. (2021, January 21\u201326). TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification. Proceedings of the Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, Virtual.","DOI":"10.1145\/3460418.3480410"},{"key":"ref_89","first-page":"4969","article-title":"ST-DeepHAR: Deep learning model for human activity recognition in IoHT applications","volume":"8","author":"Hawash","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Majidzadeh Gorjani, O., Proto, A., Vanus, J., and Bilik, P. (2020). Indirect recognition of predefined human activities. Sensors, 20.","DOI":"10.3390\/s20174829"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2022.09.099","article-title":"Two-stream transformer network for sensor-based human activity recognition","volume":"512","author":"Xiao","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_92","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 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945513"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Alexiadis, A., Nizamis, A., Giakoumis, D., Votis, K., and Tzovaras, D. (2022, January 1\u20133). A Sensor-Independent Multimodal Fusion Scheme for Human Activity Recognition. Proceedings of the Pattern Recognition and Artificial Intelligence: Third International Conference, ICPRAI 2022, Paris, France.","DOI":"10.1007\/978-3-031-09282-4_3"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dhekane, S.G., Tiwari, S., Sharma, M., and Banerjee, D.S. (2022, January 4\u20138). Enhanced annotation framework for activity recognition through change point detection. Proceedings of the 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India.","DOI":"10.1109\/COMSNETS53615.2022.9668475"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3550294","article-title":"Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch","volume":"6","author":"Hiremath","year":"2022","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Minarno, A.E., Kusuma, W.A., Wibowo, H., Akbi, D.R., and Jawas, N. (2020, January 24\u201326). Single triaxial accelerometer-gyroscope classification for human activity recognition. Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICoICT49345.2020.9166329"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"21709","DOI":"10.1007\/s11042-020-10447-x","article-title":"Human activity classification using Decision Tree and Naive Bayes classifiers","volume":"80","author":"Maswadi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/THMS.2022.3185533","article-title":"Online Change Point Detection in Application with Transition-Aware Activity Recognition","volume":"52","author":"Thakur","year":"2022","journal-title":"IEEE Trans.-Hum.-Mach. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"107040","DOI":"10.1016\/j.knosys.2021.107040","article-title":"Exploiting spatio-temporal representation for 3D human action recognition from depth map sequences","volume":"227","author":"Ji","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Albeshri, A. (2021). SVSL: A human activity recognition method using soft-voting and self-learning. Algorithms, 14.","DOI":"10.3390\/a14080245"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Aubry, S., Laraba, S., Tilmanne, J., and Dutoit, T. (2019, January 5\u20137). Action recognition based on 2D skeletons extracted from RGB videos. Proceedings of the MATEC Web of Conferences, Sibiu, Romania.","DOI":"10.1051\/matecconf\/201927702034"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Vu, D.Q., Le, N.T., and Wang, J.C. (2022, January 21\u201325). (2+1) D Distilled ShuffleNet: A Lightweight Unsupervised Distillation Network for Human Action Recognition. Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada.","DOI":"10.1109\/ICPR56361.2022.9956634"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1550147720971513","DOI":"10.1177\/1550147720971513","article-title":"Genetic algorithm\u2013optimized support vector machine for real-time activity recognition in health smart home","volume":"16","author":"Hu","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ke, W., Chan, K.H., and Xiong, Z. (2021, January 25\u201327). A Human Activity Recognition Approach Based on Skeleton Extraction and Image Reconstruction. Proceedings of the 5th International Conference on Graphics and Signal Processing, Nagoya, Japan.","DOI":"10.1145\/3474906.3474909"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TKDE.2019.2891659","article-title":"Using latent knowledge to improve real-time activity recognition for smart IoT","volume":"32","author":"Yan","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ramos, R.G., Domingo, J.D., Zalama, E., and G\u00f3mez-Garc\u00eda-Bermejo, J. (2021). Daily human activity recognition using non-intrusive sensors. Sensors, 21.","DOI":"10.3390\/s21165270"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Javed, A.R., Sarwar, M.U., Khan, S., Iwendi, C., Mittal, M., and Kumar, N. (2020). Analyzing the effectiveness and contribution of each axis of tri-axial accelerometer sensor for accurate activity recognition. Sensors, 20.","DOI":"10.3390\/s20082216"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/TIM.2019.2895931","article-title":"A knowledge-based approach for multiagent collaboration in smart home: From activity recognition to guidance service","volume":"69","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"45325","DOI":"10.1109\/ACCESS.2021.3067029","article-title":"IoT based approach for load monitoring and activity recognition in smart homes","volume":"9","author":"Franco","year":"2021","journal-title":"IEEE Access"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1007\/s12652-017-0668-0","article-title":"Ontology-based sensor fusion activity recognition","volume":"11","author":"Noor","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2020, January 11\u201314). Exercise activity recognition with surface electromyography sensor using machine learning approach. Proceedings of the 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pattaya, Thailand.","DOI":"10.1109\/ECTIDAMTNCON48261.2020.9090711"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Minarno, A.E., Kusuma, W.A., and Wibowo, H. (2020, January 26\u201329). Performance comparisson activity recognition using logistic regression and support vector machine. Proceedings of the 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore.","DOI":"10.1109\/ICoIAS49312.2020.9081858"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s12243-021-00865-9","article-title":"Wi-Sense: A passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems","volume":"77","author":"Muaaz","year":"2022","journal-title":"Ann. Telecommun."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Imran, H.A., and Latif, U. (2020, January 14\u201316). Hharnet: Taking inspiration from inception and dense networks for human activity recognition using inertial sensors. Proceedings of the 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), Charlotte, NC, USA.","DOI":"10.1109\/HONET50430.2020.9322655"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Betancourt, C., Chen, W.H., and Kuan, C.W. (2020, January 11\u201314). Self-attention networks for human activity recognition using wearable devices. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9283381"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"86411","DOI":"10.1109\/ACCESS.2020.2992584","article-title":"Human activity recognition based on improved Bayesian convolution network to analyze health care data using wearable IoT device","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Chang, J., Kang, M., and Park, D. (2022). Low-power on-chip implementation of enhanced svm algorithm for sensors fusion-based activity classification in lightweighted edge devices. Electronics, 11.","DOI":"10.3390\/electronics11010139"},{"key":"ref_118","unstructured":"LeCun, Y., Cortes, C., and Burges, C. (2023, February 06). MNIST Handwritten Digit Database. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"12350","DOI":"10.1109\/JIOT.2021.3063504","article-title":"Lightweight deep learning model in mobile-edge computing for radar-based human activity recognition","volume":"8","author":"Zhu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.future.2023.01.006","article-title":"Human activity recognition using marine predators algorithm with deep learning","volume":"142","author":"Helmi","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Angerbauer, S., Palmanshofer, A., Selinger, S., and Kurz, M. (2021). Comparing human activity recognition models based on complexity and resource usage. Appl. Sci., 11.","DOI":"10.3390\/app11188473"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Rafiq, J.I., and Islam, M.R. (2020). Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors, 20.","DOI":"10.3390\/s20010317"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:47:35Z","timestamp":1760125655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,2]]},"references-count":122,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115281"],"URL":"https:\/\/doi.org\/10.3390\/s23115281","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,2]]}}}