{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T03:04:42Z","timestamp":1767841482390,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Programa Retos Investigaci\u00f3n del Ministerio de Ciencia, Innovaci\u00f3n y Universidades","award":["RTI2018-096652-B-I00"],"award-info":[{"award-number":["RTI2018-096652-B-I00"]}]},{"name":"Programa de Apoyo a Proyectos de Investigaci\u00f3n de la Junta de Castilla y Le\u00f3n","award":["VA233P18"],"award-info":[{"award-number":["VA233P18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person\u2019s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.<\/jats:p>","DOI":"10.3390\/s21165270","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:47:52Z","timestamp":1628066872000},"page":"5270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Daily Human Activity Recognition Using Non-Intrusive Sensors"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2998-5782","authenticated-orcid":false,"given":"Ra\u00fal G\u00f3mez","family":"Ramos","sequence":"first","affiliation":[{"name":"CARTIF Technological Center, 47151 Valladolid, Spain"},{"name":"ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6649-5550","authenticated-orcid":false,"given":"Jaime Duque","family":"Domingo","sequence":"additional","affiliation":[{"name":"CARTIF Technological Center, 47151 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-5574","authenticated-orcid":false,"given":"Eduardo","family":"Zalama","sequence":"additional","affiliation":[{"name":"CARTIF Technological Center, 47151 Valladolid, Spain"},{"name":"ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4763-5356","authenticated-orcid":false,"given":"Jaime","family":"G\u00f3mez-Garc\u00eda-Bermejo","sequence":"additional","affiliation":[{"name":"CARTIF Technological Center, 47151 Valladolid, Spain"},{"name":"ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mehr, H.D., Polat, H., and Cetin, A. (2016, January 20\u201321). Resident activity recognition in smart homes by using artificial neural networks. Proceedings of the 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG), Istanbul, Turkey.","DOI":"10.1109\/SGCF.2016.7492428"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Teoh, C.C., and Tan, C.E. (2010, January 15\u201318). A neural network approach towards reinforcing smart home security. Proceedings of the 8th Asia-Pacific Symposium on Information and Telecommunication Technologies, Sarawak, Malaysia.","DOI":"10.1587\/bplus.2010.15_41"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frobt.2015.00028","article-title":"A review of human activity recognition methods","volume":"2","author":"Vrigkas","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"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), Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2014.7072750"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","article-title":"LSTM-CNN architecture for human activity recognition","volume":"8","author":"Xia","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wiseman, Y. (2010, January 15\u201317). Take a picture of your tire!. Proceedings of the 2010 IEEE International Conference on Vehicular Electronics and Safety, Qingdao, China.","DOI":"10.1109\/ICVES.2010.5550930"},{"key":"ref_7","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"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"D\u2019Sa, A.G., and Prasad, B. (2019, January 25\u201328). A survey on vision based activity recognition, its applications and challenges. Proceedings of the 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Gangtok, India.","DOI":"10.1109\/ICACCP.2019.8882896"},{"key":"ref_9","first-page":"200901","article-title":"Vision-based human action recognition: An overview and real world challenges","volume":"32","author":"Jegham","year":"2020","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_10","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":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jia, Y. (2009, January 1\u20133). Diatetic and exercise therapy against diabetes mellitus. Proceedings of the 2009 Second International Conference on Intelligent Networks and Intelligent Systems, Tianjin, China.","DOI":"10.1109\/ICINIS.2009.177"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TKDE.2007.1042","article-title":"Sensor-based abnormal human-activity detection","volume":"20","author":"Yin","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Wilson, D.H., and Atkeson, C. (2005). Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. International Conference on Pervasive Computing, Springer.","DOI":"10.1007\/11428572_5"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., and Larson, K. (2004). Activity recognition in the home using simple and ubiquitous sensors. International Conference on Pervasive Computing, Springer.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Intille, S.S., Larson, K., Beaudin, J., Nawyn, J., Tapia, E.M., and Kaushik, P. (2005, January 2\u20137). A living laboratory for the design and evaluation of ubiquitous computing technologies. Proceedings of the CHI\u201905 Extended Abstracts on Human Factors in Computing Systems, Portland, OR, USA.","DOI":"10.1145\/1056808.1057062"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alemdar, H., Ertan, H., Incel, O.D., and Ersoy, C. (2013, January 5\u20138). ARAS human activity datasets in multiple homes with multiple residents. Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, Venice, Italy.","DOI":"10.4108\/pervasivehealth.2013.252120"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tran, S.N., and Zhang, Q. (2020). Towards multi-resident activity monitoring with smarter safer home platform. Smart Assisted Living, Springer.","DOI":"10.1007\/978-3-030-25590-9_12"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Van Kasteren, T., Noulas, A., Englebienne, G., and Kr\u00f6se, B. (2008, January 21\u201324). Accurate activity recognition in a home setting. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409637"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"K\u00f6ckemann, U., Alirezaie, M., Renoux, J., Tsiftes, N., Ahmed, M.U., Morberg, D., Lind\u00e9n, M., and Loutfi, A. (2020). Open-source data collection and data sets for activity recognition in smart homes. Sensors, 20.","DOI":"10.3390\/s20030879"},{"key":"ref_21","unstructured":"Gallissot, M., Caelen, J., Bonnefond, N., Meillon, B., and Pons, S. (2011). Using the Multicom Domus Dataset. [Ph.D. Thesis, LIG]."},{"key":"ref_22","unstructured":"Cook, D., Schmitter-Edgecombe, M., Crandall, A., Sanders, C., and Thomas, B. (2009, January 4\u20139). Collecting and disseminating smart home sensor data in the CASAS project. Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, Boston, MA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Meng, Z., Zhang, M., Guo, C., Fan, Q., Zhang, H., Gao, N., and Zhang, Z. (2020). Recent progress in sensing and computing techniques for human activity recognition and motion analysis. Electronics, 9.","DOI":"10.3390\/electronics9091357"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Moreira, B.S., Perkusich, A., and Luiz, S.O. (2020). An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model. Sensors, 20.","DOI":"10.3390\/s20174803"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, P., Li, Y., Lv, X., Chen, W., and Liu, S. (2020). Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM. Sensors, 20.","DOI":"10.3390\/s20051447"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2013). Deep learning of representations: Looking forward. International Conference on Statistical Language and Speech Processing, Springer.","DOI":"10.1007\/978-3-642-39593-2_1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Du, Y., Lim, Y., and Tan, Y. (2019, January 15\u201318). Activity Prediction using LSTM in Smart Home. Proceedings of the 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan.","DOI":"10.1109\/GCCE46687.2019.9015492"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"68511","DOI":"10.1109\/ACCESS.2021.3077275","article-title":"Optimized Deep Stacked Long Short-Term Memory Network for Long-Term Load Forecasting","volume":"9","author":"Farrag","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68985","DOI":"10.1109\/ACCESS.2021.3078184","article-title":"iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition","volume":"9","author":"Ronald","year":"2021","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3992","DOI":"10.1109\/TIM.2019.2945467","article-title":"Smartphone sensor-based human activity recognition using feature fusion and maximum full a posteriori","volume":"69","author":"Chen","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","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 ESANN 2013 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Steven Eyobu, O., and Han, D.S. (2018). Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors, 18.","DOI":"10.3390\/s18092892"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.neucom.2018.10.104","article-title":"A sequential deep learning application for recognising human activities in smart homes","volume":"396","author":"Liciotti","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fang, H., and He, L. (2012, January 9\u201312). BP neural network for human activity recognition in smart home. Proceedings of the 2012 International Conference on Computer Science and Service System, Wroc\u0142aw, Poland.","DOI":"10.1109\/CSSS.2012.262"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/TSMCA.2009.2025137","article-title":"Keeping the resident in the loop: Adapting the smart home to the user","volume":"39","author":"Rashidi","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.neucom.2020.10.102","article-title":"Activity recognition and anomaly detection in smart homes","volume":"423","author":"Fahad","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_41","unstructured":"Kuchaiev, O., and Ginsburg, B. (2017). Factorization tricks for LSTM networks. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.procs.2018.08.153","article-title":"Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting","volume":"135","author":"Salman","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_43","unstructured":"Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s12559-010-9041-8","article-title":"Bidirectional LSTM networks for context-sensitive keyword detection in a cognitive virtual agent framework","volume":"2","author":"Eyben","year":"2010","journal-title":"Cogn. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, J., and Cao, Z. (2017, January 27\u201330). Chinese text sentiment analysis using LSTM network based on L2 and Nadam. Proceedings of the 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, China.","DOI":"10.1109\/ICCT.2017.8359958"},{"key":"ref_46","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Laurent, C., Pereyra, G., Brakel, P., Zhang, Y., and Bengio, Y. (2016, January 20\u201325). Batch normalized recurrent neural networks. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472159"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., and Holzinger, A. (2017). Human activity recognition using recurrent neural networks. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer.","DOI":"10.1007\/978-3-319-66808-6_18"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, L., Nugent, C.D., Biswas, J., and Hoey, J. (2011). Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. Activity Recognition in Pervasive Intelligent Environments, Atlantis Press.","DOI":"10.2991\/978-94-91216-05-3"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2012). Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. International Workshop on Ambient Assisted Living, Springer.","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Saeedi, R., Norgaard, S., and Gebremedhin, A.H. (2017, January 11\u201314). A closed-loop deep learning architecture for robust activity recognition using wearable sensors. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8257960"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:40:25Z","timestamp":1760164825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,4]]},"references-count":51,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165270"],"URL":"https:\/\/doi.org\/10.3390\/s21165270","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,4]]}}}