{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:45:41Z","timestamp":1760237141657,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T00:00:00Z","timestamp":1582848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["734355"],"award-info":[{"award-number":["734355"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated (     R  pred  2  = 92.72 %    ) if synthetic data is post-processed through Poisson regression incorporating dummy variables.<\/jats:p>","DOI":"10.3390\/rs12050771","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"771","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Simulated Data to Estimate Real Sensor Events\u2014A Poisson-Regression-Based Modelling"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-6890-7547","authenticated-orcid":false,"given":"Miguel Angel","family":"Ort\u00edz-Barrios","sequence":"first","affiliation":[{"name":"Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla 080001, Colombia"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/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:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-0882-7902","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:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-5482-6372","authenticated-orcid":false,"given":"Pablo","family":"Pancardo","sequence":"additional","affiliation":[{"name":"Academic Division of Information Science and Technology, Juarez Autonomous University of Tabasco, Tabasco 86690, Mexico"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/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, 302 20 Halmstad, Sweden"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-6768-7877","authenticated-orcid":false,"given":"Jonathan","family":"Synnott","sequence":"additional","affiliation":[{"name":"School of Computing, Computer Science Research Institute, Ulster University, Belfast BT37 0QB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"ref_1","unstructured":"Ortiz, M.A., and L\u00f3pez-Meza, P. (December, January 29). Using computer simulation to improve patient flow at an outpatient internal medicine department. Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Las Palmas de Gran Canaria, Spain."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barrios, M.A.O., Caballero, J.E., and S\u00e1nchez, F.S. (2015). A methodology for the creation of integrated service networks in outpatient internal medicine. Ambient Intelligence for Health, Springer.","DOI":"10.1007\/978-3-319-26508-7_24"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cheng, L., and Nugent, C.D. (2019). Human Activity Recognition and Behaviour Analysis, Springer Nature. [1st ed.]. Chapter Sensor-Based Activity Recognition Review.","DOI":"10.1007\/978-3-030-19408-6_2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1108\/MD-09-2017-0917","article-title":"An integrated approach to evaluate the risk of adverse events in hospital sector: From theory to practice","volume":"56","author":"Petrillo","year":"2018","journal-title":"Manag. Decis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/THMS.2016.2641388","article-title":"From activity recognition to intention recognition for assisted living within smart homes","volume":"47","author":"Rafferty","year":"2017","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nugent, C., Synnott, J., Gabrielli, C., Zhang, S., Espinilla, M., Calzada, A., Lundstrom, J., Cleland, I., Synnes, K., and Hallberg, J. (2016). Improving the quality of user generated data sets for activity recognition. Ubiquitous Computing and Ambient Intelligence, Springer.","DOI":"10.1007\/978-3-319-48799-1_13"},{"key":"ref_7","unstructured":"Helal, S., Kim, E., and Hossain, S. (2010, January 17\u201320). Scalable approaches to activity recognition research. Proceedings of the 8th International Conference Pervasive Workshop, Helsinki, Finland."},{"key":"ref_8","unstructured":"Barrios, M.O., Jim\u00e9nez, H.F., and Isaza, S.N. (2014). Comparative analysis between ANP and ANP-DEMATEL for six sigma project selection process in a healthcare provider. International Workshop on Ambient Assisted Living, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Barrios, M.O., and Jim\u00e9nez, H.F. (2015). Reduction of average lead time in outpatient service of obstetrics through six sigma methodology. Ambient Intelligence for Health, Springer.","DOI":"10.1007\/978-3-319-26508-7_29"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., and Larson, K. (2004, January 21\u201323). Activity recognition in the home using simple and ubiquitous sensors. Proceedings of the International Conference on Pervasive Computing, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"ref_11","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_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"59192","DOI":"10.1109\/ACCESS.2018.2873502","article-title":"Sensor-based datasets for human activity recognition\u2013A systematic review of literature","volume":"6","author":"Quero","year":"2018","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45473","DOI":"10.1109\/ACCESS.2018.2852656","article-title":"A Scalable, Research Oriented, Generic, Sensor Data Platform","volume":"6","author":"Rafferty","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Lundstr\u00f6m, J., Synnott, J., J\u00e4rpe, E., and Nugent, C.D. (2015, January 23\u201327). Smart home simulation using avatar control and probabilistic sampling. Proceedings of the 2015 IEEE International Conference On Pervasive Computing And Communication Workshops (Percom Workshops), St. Louis, MO, USA.","DOI":"10.1109\/PERCOMW.2015.7134059"},{"key":"ref_18","unstructured":"Ortiz-Barrios, M., Lundstr\u00f6m, J., Synnott, J., J\u00e4rpe, E., and Sant\u2019Anna, A. Complementing real datasets with simulated data: A regression-based approach. Multimedia Tools and Applications, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/S0167-2789(00)00043-9","article-title":"Surrogate time series","volume":"142","author":"Schreiber","year":"2000","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Maiwald, T., Mammen, E., Nandi, S., and Timmer, J. (2008). Surrogate data\u2014A qualitative and quantitative analysis. Mathematical Methods in Signal Processing and Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-540-75632-3_2"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Salazar, A., Safont, G., and Vergara, L. (2014, January 13\u201316). Surrogate techniques for testing fraud detection algorithms in credit card operations. Proceedings of the 2014 International Carnahan Conference on Security Technology (ICCST), Rome, Italy.","DOI":"10.1109\/CCST.2014.6986987"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1007\/s00134-008-1062-3","article-title":"The effect of prone positioning in acute respiratory distress syndrome or acute lung injury: A meta-analysis. Areas of uncertainty and recommendations for research","volume":"34","author":"Abroug","year":"2008","journal-title":"Intensive Care Med."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","unstructured":"Francillette, Y., Boucher, E., Bouzouane, A., and Gaboury, S. (2017). The Virtual Environment for Rapid Prototyping of the Intelligent Environment. Sensors, 17.","DOI":"10.3390\/s17112562"},{"key":"ref_26","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. Its Appl."},{"key":"ref_27","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_28","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. UCS"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Renoux, J., and Klugl, F. (2018, January 9\u201312). Simulating daily activities in a smart home for data generation. Proceedings of the 2018 Winter Simulation Conference (WSC), G\u00f6teborg, Sweden.","DOI":"10.1109\/WSC.2018.8632226"},{"key":"ref_30","unstructured":"Mendez-Vazquez, A., Helal, A., and Cook, D. (2009). Simulating events to generate synthetic data for pervasive spaces. Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, Available online: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/pdfs.semanticscholar.org\/a7ce\/e34ebf272ba18eb60f1a23bd713890890e0c.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cameron, A. (1998). Regression Analysis of Count Data, Cambridge University Press.","DOI":"10.1017\/CBO9780511814365"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.insmatheco.2005.11.006","article-title":"Modelling negatives in stochastic reserving models","volume":"38","author":"Kunkler","year":"2006","journal-title":"Insur. Math. Econ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Andersson, P.K., and Skovgaard, L.T. (2010). Regression with Linear Predictors, Springer.","DOI":"10.1007\/978-1-4419-7170-8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1002\/bimj.200410102","article-title":"Generalized Poisson distribution: The property of mixture of Poisson and comparison with negative binomial distribution","volume":"47","author":"Joe","year":"2005","journal-title":"Biom. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/03610929208830766","article-title":"Generalized Poisson regression-model","volume":"21","author":"Consul","year":"1992","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_36","first-page":"219","article-title":"Evaluating the Anderson-Darling Distribution","volume":"9","author":"Marsaglia","year":"2005","journal-title":"J. Stat. Softw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1093\/biomet\/65.2.297","article-title":"On a Measure of a Lack of Fit in Time Series Models","volume":"65","author":"Ljung","year":"1978","journal-title":"Biometrika"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lundstr\u00f6m, J., De Morais, W.O., 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, V\u00e4ster\u00e5s, Sweden.","DOI":"10.1007\/978-3-319-51234-1_2"},{"key":"ref_39","unstructured":"Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications, Academic Press."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2009). Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/12\/5\/771\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:02:41Z","timestamp":1760173361000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/12\/5\/771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,28]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050771"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/rs12050771","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,2,28]]}}}