{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:33:26Z","timestamp":1776285206021,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020","doi-asserted-by":"publisher","award":["734355"],"award-info":[{"award-number":["734355"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person\u2019s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value &lt; 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)&gt;90%).<\/jats:p>","DOI":"10.3390\/s22145410","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T03:34:40Z","timestamp":1658374480000},"page":"5410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6890-7547","authenticated-orcid":false,"given":"Miguel","family":"Ortiz-Barrios","sequence":"first","affiliation":[{"name":"Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 08002, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9307-9421","authenticated-orcid":false,"given":"Eric","family":"J\u00e4rpe","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems and Digital Design, Halmstad University, P.O. Box 823, S 301 18 Halmstad, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3420-0532","authenticated-orcid":false,"given":"Mat\u00edas","family":"Garc\u00eda-Constantino","sequence":"additional","affiliation":[{"name":"School of Computing, Computer Science Research Institute, Ulster University, Belfast BT37 0QB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-7354","authenticated-orcid":false,"given":"Ian","family":"Cleland","sequence":"additional","affiliation":[{"name":"School of Computing, Computer Science Research Institute, Ulster University, Belfast BT37 0QB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6295-8669","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"School of Computing, Computer Science Research Institute, Ulster University, Belfast BT37 0QB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5067-2839","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Arias-Fonseca","sequence":"additional","affiliation":[{"name":"Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 08002, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8362-1552","authenticated-orcid":false,"given":"Natalia","family":"Jaramillo-Rueda","sequence":"additional","affiliation":[{"name":"Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 08002, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, April 28). World Health Organisation Dementia Key Facts. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1111\/j.1442-2018.2005.00213.x","article-title":"Caring for a person with dementia: Exploring relationships between perceived burden, depression, coping and well-being","volume":"7","author":"McConaghy","year":"2005","journal-title":"Nurs. Health Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1177\/1471301217691617","article-title":"Technology-based tools and services for people with dementia and carers: Mapping technology onto the dementia care pathway","volume":"18","author":"Lorenz","year":"2019","journal-title":"Dementia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"798889","DOI":"10.3389\/fdgth.2021.798889","article-title":"Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents with Dementia in Care Homes","volume":"3","author":"Orr","year":"2021","journal-title":"Front. Digit. Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ort\u00edz-Barrios, M.A., Garcia-Constantino, M., Nugent, C., and Alfaro-Sarmiento, I. (2022). A Novel Integration of IF-DEMATEL and TOPSIS for the Classifier Selection Problem in Assistive Technology Adoption for People with Dementia. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19031133"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34301","DOI":"10.1007\/s11042-019-08368-5","article-title":"Complementing real datasets with simulated data: A regression-based approach","volume":"79","author":"Synnott","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dahmen, J., and Cook, D. (2019). SynSys: A synthetic data generation system for healthcare applications. Sensors, 19.","DOI":"10.3390\/s19051181"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s42979-021-00708-3","article-title":"Data-driven forecasting of agitation for persons with dementia: A deep learning-based approach","volume":"2","author":"HekmatiAthar","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"42084","DOI":"10.1038\/srep42084","article-title":"Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living","volume":"7","author":"Urwyler","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Damla, A., Wang, Y., and Bouchachia, A. (2021). Detection of dementia-related abnormal behaviour using recursive auto-encoders. Sensors, 21.","DOI":"10.3390\/s21010260"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Enshaeifar, S., Zoha, A., Skillman, S., Markides, A., Acton, S.T., Elsaleh, T., Kenny, M., Rostill, H., Nilforooshan, R., and Barnaghi, P. (2019). Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0209909"},{"key":"ref_12","unstructured":"Virone, G., Lefebvre, B., Noury, N., and Demongeot, J. (2003, January 7). Modeling and computer simulation of physiological rhythms and behaviors at home for data fusion programs in a telecare system. Proceedings of the 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry, Santa Monica, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Helal, A., Mendez-Vazquez, A., and Hossain, S. (2009, January 5\u20138). Specification and synthesis of sensory datasets in pervasive spaces. Proceedings of the 2009 IEEE Symposium on Computers and Communications, Sousse, Tunisia.","DOI":"10.1109\/ISCC.2009.5202263"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alharbi, F., Ouarbya, L., and Ward, J.A. (2020, January 19\u201324). Synthetic sensor data for human activity recognition. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206624"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TASE.2015.2467353","article-title":"Persim 3d: Context-driven simulation and modeling of human activities in smart spaces","volume":"12","author":"Lee","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.pmcj.2017.07.007","article-title":"MASSHA: An agent-based approach for human activity simulation in intelligent environments","volume":"40","author":"Azkune","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alshammari, N., Alshammari, T., Sedky, M., Champion, J., and Bauer, C. (2017). Openshs: Open smart home simulator. Sensors, 17.","DOI":"10.3390\/s17051003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Damien, B., Nguyen, S.M., Lohr, C., LeDuc, B., and Kanellos, I. (2021). A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning. Sensors, 21.","DOI":"10.3390\/s21186037"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10462-017-9569-z","article-title":"Parallel vision for perception and understanding of complex scenes: Methods, framework and perspectives","volume":"48","author":"Wang","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reeves, D.R., and Taylor, S.J. (1998, January 27\u201330). Selection of training data for neural networks by a genetic algorithm. Proceedings of the International Conference on Parallel Problem Solving from Nature 1998, Amsterdam, The Netherlands.","DOI":"10.1007\/BFb0056905"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0377-2217(94)00016-6","article-title":"Verification and validation of simulation models","volume":"82","author":"Kleijnen","year":"1995","journal-title":"Eur. J. Oper. Res."},{"key":"ref_22","unstructured":"Wold, H. (1966). Estimation of principal components and related models by iterative least squares. Multivariate Analysis, Academic Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/0003-2670(86)80024-1","article-title":"Selectivity in multicomponent analysis","volume":"180","author":"Otto","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pirouz, D.M. (2006). An Overview of Partial Least Squares. ERN: Other Econometrics: Econometric & Statistical Methods (Topic), SSRN.","DOI":"10.2139\/ssrn.1631359"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kowalski, B. (1984). Multivarite Data Analysis in Chemistry. Chemometrics: Mathematics and Statistics, D. Riedel Publishing Company.","DOI":"10.1007\/978-94-017-1026-8"},{"key":"ref_27","unstructured":"Martens, W., and Rosswurm, H. (1984). Food Research and Data Analysis, Applied Science Publishers."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression\u2014A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TBME.2018.2847404","article-title":"Fast Multiway Partial Least Squares Regression","volume":"66","author":"Camarrone","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s00180-009-0164-x","article-title":"On the convergence of the partial least squares path modeling algorithm","volume":"25","author":"Henseler","year":"2010","journal-title":"Comput. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.chemolab.2015.04.014","article-title":"Variance constrained partial least squares","volume":"145","author":"Jiang","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.chemolab.2016.10.013","article-title":"Multiview Partial Least Squares","volume":"160","author":"Mou","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1093\/biomet\/asw010","article-title":"Partial least squares for dependent data","volume":"103","author":"Singer","year":"2016","journal-title":"Biometrika"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e3044","DOI":"10.1002\/cem.3044","article-title":"Model and estimators for partial least squares regression","volume":"32","author":"Helland","year":"2018","journal-title":"J. Chemom."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/1467-9868.00252","article-title":"The peculiar shrinkage properties of partial least squares regression","volume":"62","author":"Butler","year":"2000","journal-title":"J. R. Stat. Soc. B"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0169-7439(95)00062-3","article-title":"Partial least squares and compositional data: Problems and alternatives","volume":"30","author":"Hinkle","year":"1995","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","first-page":"9","article-title":"Partial least squares path modeling: Time for some serious second thoughts","volume":"47\u201348","author":"McIntosh","year":"2016","journal-title":"J. Oper. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Lytras, M.D., and Vasant, P. (2020). Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset. Appl. Sci., 10.","DOI":"10.3390\/app10134571"},{"key":"ref_39","first-page":"1","article-title":"How Generative Adversarial Networks and Their Variants Work: An Overview","volume":"52","author":"Hong","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11831-019-09388-y","article-title":"Applications of Generative Adversarial Networks (GANs): An Updated Review","volume":"28","author":"Alqahtani","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_41","unstructured":"Hui, J. (2022, July 01). GAN\u2014Why It Is So Hard to Train Generative Adversarial Networks! Medium. Available online: https:\/\/jonathan-hui.medium.com\/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Luo, F.-L. (2020). Machine Learning for Future Wireless Communications, John Wiley & Sons.","DOI":"10.1002\/9781119562306"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3726","DOI":"10.1016\/j.patcog.2014.05.022","article-title":"Incremental partial least squares analysis of big streaming data","volume":"47","author":"Zeng","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_44","unstructured":"Kearns, M.J. (1990). The Computational Complexity of Machine Learning, MIT Press."},{"key":"ref_45","unstructured":"Tang, Z., Luo, L., Xie, B., Zhu, Y., Zhao, R., Bi, L., and Lu, C. (2022). Automatic sparse connectivity learning for neural networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315."},{"key":"ref_46","first-page":"180","article-title":"Weight-Quantized SqueezeNet for Resource-Constrained Robot Vacuums for Indoor Obstacle Classification","volume":"3","author":"Huang","year":"2022","journal-title":"Artif. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.3389\/fnins.2019.01282","article-title":"Partial least squares regression performs well in MRI-based individualized estimations","volume":"13","author":"Chen","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hao, C., and Chen, D. (2021, January 6\u20139). Software\/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems. Proceedings of the IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington, DC, USA.","DOI":"10.1109\/AICAS51828.2021.9458577"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TCDS.2020.2965166","article-title":"Improving the Generalization Ability of Deep Neural Networks for Cross-Domain Visual Recognition","volume":"13","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/00401706.1974.10489157","article-title":"The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction","volume":"16","author":"Allen","year":"1974","journal-title":"Technometrics"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.chemolab.2017.08.006","article-title":"A new model selection criterion for partial least squares regression","volume":"169","author":"Saulo","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0167-9473(98)00088-7","article-title":"PRESS model selection in repeated measures data","volume":"30","author":"Liu","year":"1999","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pratt, J.W., and Gibbons, J.D. (1981). Concepts of Nonparametric Theory, Springer.","DOI":"10.1007\/978-1-4612-5931-2"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0167-7152(95)00164-H","article-title":"Edgeworth approximations for rank sum test statistics","volume":"24","author":"Kolassa","year":"1995","journal-title":"Stat. Probab. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cox, D.R., and Hinkley, D.V. (1974). Theoretical Statistics, Chapman & Hall.","DOI":"10.1007\/978-1-4899-2887-0"},{"key":"ref_57","unstructured":"Larsen, R.J., and Marx, M.L. (2018). An Introduction to Mathematical Statistics and Its Applications, Pearson. [6th ed.]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lundstr\u00f6m, J., Morais, W.O.D., Menezes, M., Gabrielli, C., Bentes, J., Sant\u2019Anna, A., Synnott, J., and Nugent, C. (2016, January 18\u201319). Halmstad intelligent home-capabilities and opportunities. Proceedings of the International Conference on IoT Technologies for HealthCare, Budapest, Hungary.","DOI":"10.1007\/978-3-319-51234-1_2"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hamad, R.A., J\u00e4rpe, E., and Lundstr\u00f6m, J. (2018, January 7\u201310). Stability analysis of the t-SNE algorithm for human activity pattern data. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00318"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"14162","DOI":"10.3390\/s150614162","article-title":"Simulation of smart home activity datasets","volume":"15","author":"Synnott","year":"2015","journal-title":"Sensors"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1111\/j.1532-5415.1983.tb03391.x","article-title":"Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living","volume":"31","author":"Katz","year":"1983","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_63","first-page":"53","article-title":"The User Activity Reasoning Model in a Virtual Living Space Simulator","volume":"9","author":"Park","year":"2015","journal-title":"Int. J. Softw. Eng. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Synnott, J., Chen, L., Nugent, C.D., and Moore, G. (2014, January 26\u201330). The creation of simulated activity datasets using a graphical intelligent environment simulation tool. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944536"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ariani, A., Redmond, S.J., Chang, D., and Lovell, N.H. (2013, January 7\u20138). Simulation of a smart home environment. Proceedings of the 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), Bandung, Indonesia.","DOI":"10.1109\/ICICI-BME.2013.6698459"},{"key":"ref_66","first-page":"1992","article-title":"SimCon: A Tool to Support Rapid Evaluation of Smart Building Application Design using Context Simulation and Virtual Reality","volume":"16","author":"McGlinn","year":"2010","journal-title":"J. Univers. Comput. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"107567","DOI":"10.1016\/j.compeleceng.2021.107567","article-title":"Predicting activities of daily living via temporal point processes: Approaches and experimental results","volume":"96","author":"Fortino","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/JSEN.2020.3018335","article-title":"FallAllD: An open dataset of human falls and activities of daily living for classical and deep learning applications","volume":"21","author":"Saleh","year":"2020","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5410\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:54:31Z","timestamp":1760140471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":68,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145410"],"URL":"https:\/\/doi.org\/10.3390\/s22145410","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}