{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:23:43Z","timestamp":1779099823817,"version":"3.51.4"},"reference-count":84,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"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>In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.<\/jats:p>","DOI":"10.3390\/s20010216","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T03:28:53Z","timestamp":1578022133000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments"],"prefix":"10.3390","volume":"20","author":[{"given":"Naomi","family":"Irvine","sequence":"first","affiliation":[{"name":"School of Computing, Ulster University, Co. Antrim, Northern Ireland BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0882-7902","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Co. Antrim, Northern Ireland BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Co. Antrim, Northern Ireland BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Co. Antrim, Northern Ireland BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0783-3585","authenticated-orcid":false,"given":"Wing W. Y.","family":"NG","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1550147716665520","DOI":"10.1177\/1550147716665520","article-title":"A review on applications of activity recognition systems with regard to performance and evaluation","volume":"12","author":"Ranasinghe","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/10095020.2018.1441754","article-title":"Geometric-constrained multi-view image matching method based on semi-global optimization","volume":"21","author":"Zhao","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.neucom.2014.09.074","article-title":"Adaptive mobile activity recognition system with evolving data streams","volume":"150","author":"Abdallah","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1080\/10095020.2017.1413798","article-title":"A spatiotemporal algebra in Hadoop for moving objects","volume":"21","author":"Bakli","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s11676-017-0528-y","article-title":"Forest mapping: A comparison between hyperspectral and multispectral images and technologies","volume":"29","author":"Awad","year":"2018","journal-title":"J. For. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jalal, A., Quaid, M.A.K., and Hasan, A.S. (2018, January 17\u201319). Wearable sensor-based human behavior understanding and recognition in daily life for smart environments. Proceedings of the 2018 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan.","DOI":"10.1109\/FIT.2018.00026"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1049\/trit.2017.0002","article-title":"Multi-objective evolutionary approach to select security solutions","volume":"2","author":"Lee","year":"2017","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_8","first-page":"62","article-title":"CASAS: A Smart Home in a Box","volume":"46","author":"Cook","year":"2013","journal-title":"Computing Practices"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Helal, S., and Chen, C. (2009, January 22\u201326). The Gator Tech Smart House: Enabling Technologies and Lessons Learned. Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology, Singapore.","DOI":"10.1145\/1592700.1592715"},{"key":"ref_10","unstructured":"Cook, D.J., Youngblood, M., Heierman, E.O., Gopalratnam, K., Rao, S., Litvin, A., and Khawaja, F. (2003, January 26). MavHome: An Agent-Based Smart Home. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003 (PerCom 2003), Fort Worth, TX, USA."},{"key":"ref_11","unstructured":"(2019, November 08). The DOMUS Laboratory. Available online: http:\/\/domuslab.fr."},{"key":"ref_12","unstructured":"(2019, November 08). The Aware Home. Available online: http:\/\/awarehome.imtc.gatech.edu."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","article-title":"Activity Recognition on Streaming Sensor Data","volume":"10","author":"Krishnan","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.5370\/JEET.2016.11.6.1857","article-title":"Depth images-based human detection, tracking and activity recognition using spatiotemporal features and modified HMM","volume":"11","author":"Kamal","year":"2016","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jvcir.2013.03.011","article-title":"An adaptable system for RGB-D based human body detection and pose estimation","volume":"25","author":"Buys","year":"2014","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-Based Activity Recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1016\/j.eswa.2014.11.063","article-title":"Extending Knowledge-Driven Activity Models through Data-Driven Learning Techniques","volume":"42","author":"Azkune","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cleland, I., Donnelly, M.P., Nugent, C.D., Hallberg, J., and Espinilla, M. (2018, January 19\u201323). Collection of a Diverse, Naturalistic and Annotated Dataset for Wearable Activity Recognition. Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece.","DOI":"10.1109\/PERCOMW.2018.8480322"},{"key":"ref_19","unstructured":"Akhand, M.A.H., and Murase, K. (2010). Neural Networks Ensembles: Existing Methods and New Techniques, LAP LAMBERT Academic Publishing."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sharkey, A.J.C. (1999). Combining Artificial Neural Nets, Springer.","DOI":"10.1007\/978-1-4471-0793-4"},{"key":"ref_21","unstructured":"Aggarwal, J.K., Xia, L., Ann, O.C., and Theng, L.B. (2014, January 28\u201330). Human activity recognition: A review. Proceedings of the 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), Batu Ferringhi, Malaysia."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/JBHI.2017.2734803","article-title":"Automatic Recognition of Activities of Daily Living utilizing Insole-Based and Wrist-Worn Wearable Sensors","volume":"22","author":"Hegde","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8934816","DOI":"10.1155\/2017\/8934816","article-title":"Classification of Daily Activities for the Elderly Using Wearable Sensors","volume":"2017","author":"Liu","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pirsiavash, H., and Ramanan, D. (2012, January 16\u201321). Detecting activities of Daily Living in First-Person Camera Views. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248010"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-015-0294-7","article-title":"Ambient and Smartphone Sensor Assisted ADL Recognition in Multi-Inhabitant Smart Environments","volume":"7","author":"Roy","year":"2016","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moriya, K., Nakagawa, E., Fujimoto, M., Suwa, H., Arakawa, Y., Kimura, A., Miki, S., and Yasumoto, K. (2017, January 13\u201317). Daily Living Activity Recognition with Echonet Lite Appliances and Motion Sensors. Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA.","DOI":"10.1109\/PERCOMW.2017.7917603"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gochoo, M., Tan, T., and Huang, S. (2017, January 21\u201323). DCNN-Based Elderly Activity Recognition Using Binary Sensors. Proceedings of the 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates.","DOI":"10.1109\/ICECTA.2017.8252040"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Singh, D., Merdivan, E., and Hanke, S. (2017). Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment. Towards Integrative Machine Learning and Knowledge Extraction, Springer.","DOI":"10.1007\/978-3-319-69775-8_12"},{"key":"ref_29","unstructured":"Cook, D.J., and Krishnan, N.C. (2015). Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data, Wiley. [1st ed.]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1249\/MSS.0000000000001144","article-title":"Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle","volume":"49","author":"Mannini","year":"2017","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_31","unstructured":"Huang, Q., Yang, J., and Qiao, Y. (November, January 30). Person re-identification across multi-camera system based on local descriptors. Proceedings of the 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC), Hong Kong, China."},{"key":"ref_32","first-page":"1856","article-title":"Dense RGB-D map-based human tracking and activity recognition using skin joints features and self-organizing map","volume":"9","author":"Farooq","year":"2015","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s13369-015-1955-8","article-title":"A Hybrid Feature Extraction Approach for Human Detection, Tracking and Activity Recognition Using Depth Sensors","volume":"41","author":"Kamal","year":"2016","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"B\u00f6ttcher, S., Scholl, P.M., and van Laerhoven, K. (2018). Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach. Informatics, 5.","DOI":"10.3390\/informatics5020016"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1249\/MSS.0b013e318258ac11","article-title":"Artificial Neural Networks to Predict Activity Type and Energy Expenditure in Youth","volume":"44","author":"Trost","year":"2012","journal-title":"Med. Sci. Sport Exerc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A Survey on Human Activity Recognition using Wearable Sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Synnott, J., Nugent, C., Zhang, S., Calzada, A., Cleland, I., Espinilla, M., Quero, J.M., and Lundstrom, J. (2016, January 18\u201320). Environment Simulation for the Promotion of the Open Data Initiative. Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, USA.","DOI":"10.1109\/SMARTCOMP.2016.7501690"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1515\/msr-2015-0044","article-title":"Optimal Recognition Method of Human Activities Using Artificial Neural Networks","volume":"15","author":"Oniga","year":"2015","journal-title":"Meas. Sci. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1145\/2967979","article-title":"GPUs Reshape Computing","volume":"59","author":"Greengard","year":"2016","journal-title":"Commun. ACM"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cogsys.2018.11.009","article-title":"Efficiency investigation from shallow to deep neural network techniques in human activity recognition","volume":"54","author":"Suto","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_41","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013). A Public Domain Dataset for Human Activity Recognition Using Smartphones. Eur. Symp. Artif. Neural Netw., 437\u2013442. 9782874190827."},{"key":"ref_42","first-page":"777","article-title":"Ensemble Learning for Regression","volume":"2","author":"Rooney","year":"2010","journal-title":"Encyclopedia Data Warehous. Mining Inf. Sci. Ref. N. Y. US"},{"key":"ref_43","first-page":"10","article-title":"Ensemble Approaches for Regression: A Survey","volume":"45","author":"Soares","year":"2012","journal-title":"ACM Comput. Surv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fatima, I., Fahim, M., Lee, Y.-K., and Lee, S. (2013, January 17\u201319). Classifier ensemble optimization for human activity recognition in smart homes. Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, Kota Kinabalu, Malaysia.","DOI":"10.1145\/2448556.2448639"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ni, Q., Zhang, L., and Li, L. (2018). A Heterogeneous Ensemble Approach for Activity Recognition with Integration of Change Point-Based Data Segmentation. Appl. Sci., 8.","DOI":"10.3390\/app8091695"},{"key":"ref_46","unstructured":"Feng, Z., Mo, L., and Li, M. (2015, January 25\u201329). A Random Forest-based ensemble method for activity recognition. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Kang, B.N., and Kim, D. (2016, January 9\u201312). Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China.","DOI":"10.1109\/SMC.2015.528"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1016\/j.patrec.2013.02.014","article-title":"On-line anomaly detection and resilience in classifier ensembles","volume":"34","author":"Sagha","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"(2013). A Genetic Algorithm-based Classifier Ensemble Optimization for Activity Recognition in Smart Homes. KSII Trans. Internet Inf. Syst., 7, 2853\u20132873.","DOI":"10.3837\/tiis.2013.11.018"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"JMin, K., and Cho, S.B. (2011, January 9\u201312). Activity recognition based on wearable sensors using selection\/fusion hybrid ensemble. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083808"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Diep, N.N., Pham, C., and Phuong, T.M. (2016). Motion Primitive Forests for Human Activity Recognition Using Wearable Sensors. Pacific Rim International Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-319-42911-3_29"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/j.asoc.2015.08.025","article-title":"Hybrid deep neural network model for human action recognition","volume":"46","author":"Ijjina","year":"2016","journal-title":"Appl. Soft Comput. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csl.2015.11.003","article-title":"Ensemble of deep neural networks using acoustic environment classification for statistical model-based voice activity detection","volume":"38","author":"Hwang","year":"2016","journal-title":"Comput. Speech Lang."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/3090076","article-title":"Ensembles of Deep LSTM Learners for Activity Recognition using Wearables","volume":"1","author":"Guan","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","article-title":"Data fusion and multiple classifier systems for human activity detection and health monitoring","volume":"46","author":"Nweke","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.inffus.2013.04.006","article-title":"A survey of multiple classifier systems as hybrid systems","volume":"16","author":"Corchado","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.knosys.2015.04.022","article-title":"Random Balance: Ensembles of variable priors classifiers for imbalanced data","volume":"85","author":"Kuncheva","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Feng, W., Huang, W., and Ren, J. (2018). Class imbalance ensemble learning based on the margin theory. Appl. Sci., 8.","DOI":"10.3390\/app8050815"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Mahbub, A., Rayhan, F., Jani, R., Shatabda, S., and Farid, D.M. (2017, January 21\u201323). Hybrid Methods for Class Imbalance Learning Employing Bagging with Sampling Techniques. Proceedings of the 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bangalore, India.","DOI":"10.1109\/CSITSS.2017.8447799"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2016, January 16\u201320). Detection of chewing from piezoelectric film sensor signals using ensemble classifiers. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591833"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mohammadi, E., Wu, Q.M.J., and Saif, M. (2016, January 18\u201322). Human activity recognition using an ensemble of support vector machines. Proceedings of the 2016 International Conference on High Performance Computing & Simulation (HPCS), Innsbruck, Austria.","DOI":"10.1109\/HPCSim.2016.7568383"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.procs.2014.07.009","article-title":"A study on human activity recognition using accelerometer data from smartphones","volume":"34","author":"Bayat","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Tr\u00f6ster, G. (2007, January 3\u20136). Activity recognition from on-body sensors by classifier fusion: Sensor scalability and robustness. Proceedings of the 2007 3rd international conference on intelligent sensors, sensor networks and information, Melbourne, QLD, Australia.","DOI":"10.1109\/ISSNIP.2007.4496857"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neucom.2016.02.040","article-title":"Untrained weighted classifier combination with embedded ensemble pruning","volume":"196","author":"Krawczyk","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_65","first-page":"1","article-title":"A Survey on Data Collection for Machine Learning: A Big Data\u2014AI Integration Perspective","volume":"1","author":"Roh","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques, Springer. [1st ed.].","DOI":"10.1007\/978-3-642-19721-5"},{"key":"ref_67","unstructured":"Kantardzic, M. (2002). Data Mining: Concepts, Models, Methods and Algorithms, Wiley-IEEE Press. [2nd ed.]."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2005). Data Mining and Knowledge Discovery Handbook, Springer. [1st ed.].","DOI":"10.1007\/b107408"},{"key":"ref_69","unstructured":"Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques, Elsevier Inc.. [3rd ed.]."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","article-title":"Class Noise vs. Attribute Noise: A Quantitative Study of Their Impacts","volume":"22","author":"Zhu","year":"2004","journal-title":"Artif. Intell. Rev."},{"key":"ref_71","first-page":"245","article-title":"Identifying Learners Robust to Low Quality Data","volume":"33","author":"Folleco","year":"2009","journal-title":"Informatica"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Espinilla, M., Medina, J., and Nugent, C. (2018). UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living. MDPI Proc. UCAmI, 2.","DOI":"10.3390\/proceedings2191267"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Karvonen, N., and Kleyko, D. (2018). A Domain Knowledge-Based Solution for Human Activity Recognition: The UJA Dataset Analysis. MDPI Proc. UCAmI, 2.","DOI":"10.3390\/proceedings2191261"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lago, P., and Inoue, S. (2018). A Hybrid Model Using Hidden Markov Chain and Logic Model for Daily Living Activity Recognition. MDPI Proc. UCAmI, 2.","DOI":"10.3390\/proceedings2191266"},{"key":"ref_75","first-page":"1240","article-title":"Event-Driven Real-Time Location-Aware Activity Recognition in AAL Scenarios","volume":"2","author":"Seco","year":"2018","journal-title":"MDPI Proc. UCAmI"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Cer\u00f3n, J.D., L\u00f3pez, D.M., and Eskofier, B.M. (2018). Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach. MDPI Proc. UCAmI, 2.","DOI":"10.3390\/proceedings2191265"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.maturitas.2011.03.016","article-title":"Sensor technology for smart homes","volume":"69","author":"Ding","year":"2011","journal-title":"Maturitas"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s12652-015-0270-2","article-title":"A review of smart homes in healthcare","volume":"6","author":"Amiribesheli","year":"2015","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"116","DOI":"10.15623\/ijret.2013.0211019","article-title":"DATA MINING TECHNIQUES: A SURVEY PAPER","volume":"2","author":"Jain","year":"2013","journal-title":"Int. J. Res. Eng. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/2499621","article-title":"A Tutorial on Human Activity Recognition using Body-Worn Inertial Sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window Size Impact in Human Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"19626","DOI":"10.1109\/ACCESS.2018.2813079","article-title":"Classifiers Combination Techniques: A Comprehensive Review","volume":"6","author":"Mohandes","year":"2018","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.knosys.2015.11.013","article-title":"Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data","volume":"94","author":"Yijing","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Suto, J., Oniga, S., and Sitar, P.P. (2016, January 10\u201314). Comparison of wrapper and filter feature selection algorithms on human activity recognition. Proceedings of the 2016 6th International Conference on Computers Communications and Control (ICCCC), Oradea, Romania.","DOI":"10.1109\/ICCCC.2016.7496749"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:46:55Z","timestamp":1760190415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,30]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010216"],"URL":"https:\/\/doi.org\/10.3390\/s20010216","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,30]]}}}