Simulated data to estimate real sensor events—a poisson-regression-based modelling

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. A...

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Autores:
Ortiz Barrios, Miguel Angel
Cleland, Ian
Nugent, Chris
Pancardo, Pablo
Järpe, Eric
Synnott, Jonathan
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6174
Acceso en línea:
https://hdl.handle.net/11323/6174
https://repositorio.cuc.edu.co/
Palabra clave:
Activity recognition
Activities of daily living (ADL)
Digital simulation
Poisson regression
Large-scale datasets
Sensor systems
Smart homes
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_1a85b9ebf6892b197c225db01677193d
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6174
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Simulated data to estimate real sensor events—a poisson-regression-based modelling
title Simulated data to estimate real sensor events—a poisson-regression-based modelling
spellingShingle Simulated data to estimate real sensor events—a poisson-regression-based modelling
Activity recognition
Activities of daily living (ADL)
Digital simulation
Poisson regression
Large-scale datasets
Sensor systems
Smart homes
title_short Simulated data to estimate real sensor events—a poisson-regression-based modelling
title_full Simulated data to estimate real sensor events—a poisson-regression-based modelling
title_fullStr Simulated data to estimate real sensor events—a poisson-regression-based modelling
title_full_unstemmed Simulated data to estimate real sensor events—a poisson-regression-based modelling
title_sort Simulated data to estimate real sensor events—a poisson-regression-based modelling
dc.creator.fl_str_mv Ortiz Barrios, Miguel Angel
Cleland, Ian
Nugent, Chris
Pancardo, Pablo
Järpe, Eric
Synnott, Jonathan
dc.contributor.author.spa.fl_str_mv Ortiz Barrios, Miguel Angel
Cleland, Ian
Nugent, Chris
Pancardo, Pablo
Järpe, Eric
Synnott, Jonathan
dc.subject.spa.fl_str_mv Activity recognition
Activities of daily living (ADL)
Digital simulation
Poisson regression
Large-scale datasets
Sensor systems
Smart homes
topic Activity recognition
Activities of daily living (ADL)
Digital simulation
Poisson regression
Large-scale datasets
Sensor systems
Smart homes
description 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 (R2pred = 92.72%) if synthetic data is post-processed through Poisson regression incorporating dummy variables.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-13T15:08:49Z
dc.date.available.none.fl_str_mv 2020-04-13T15:08:49Z
dc.date.issued.none.fl_str_mv 2020-02-28
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 2072-4292
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6174
dc.identifier.doi.spa.fl_str_mv doi:10.3390/rs12050771
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2072-4292
doi:10.3390/rs12050771
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6174
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Ortiz, M.A.; López-Meza, P. Using computer simulation to improve patient flow at an outpatient internal medicine department. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Las Palmas de Gran Canaria, Spain, 29 November–2 December 2016; Springer: Cham, Switzerland, 2016; pp. 294–299.
2. Barrios, M.A.O.; Caballero, J.E.; Sánchez, F.S. A methodology for the creation of integrated service networks in outpatient internal medicine. In Ambient Intelligence for Health; Springer: Cham, Switzerland, 2015 ; pp. 247–257.
3. Cheng, L.; Nugent, C.D. Human Activity Recognition and Behaviour Analysis, 1st ed.; ; Chapter Sensor-Based Activity Recognition Review; Springer Nature: Cham, Switzerland, 2019.
4. Ortiz-Barrios, M.A.; Herrera-Fontalvo, Z.; Rúa-Muñoz, J.; Ojeda-Gutiérrez, S.; De Felice, F.; Petrillo, A. An integrated approach to evaluate the risk of adverse events in hospital sector: From theory to practice. Manag. Decis. 2018, 56, 2187–2224. [CrossRef]
5. Rafferty, J.; Nugent, C.D.; Liu, J.; Chen, L. From activity recognition to intention recognition for assisted living within smart homes. IEEE Trans. Hum.-Mach. Syst. 2017, 47, 368–379. [CrossRef]
6. Nugent, C.; Synnott, J.; Gabrielli, C.; Zhang, S.; Espinilla, M.; Calzada, A.; Lundstrom, J.; Cleland, I.; Synnes, K.; Hallberg, J.; et al. Improving the quality of user generated data sets for activity recognition. In Ubiquitous Computing and Ambient Intelligence; Springer: Cham, Switzerland, 2016; pp. 104–110.
7. Helal, S.; Kim, E.; Hossain, S. Scalable approaches to activity recognition research. In Proceedings of the 8 th International Conference Pervasive Workshop, Helsinki, Finland, 17–20 May 2010; pp. 450–453.
8. Barrios, M.O.; Jiménez, H.F.; Isaza, S.N. Comparative analysis between ANP and ANP-DEMATEL for six sigma project selection process in a healthcare provider. In International Workshop on Ambient Assisted Living; Springer: Cham, Switzerland, 2014; pp. 413–416.
9. Barrios, M.O.; Jiménez, H.F. Reduction of average lead time in outpatient service of obstetrics through six sigma methodology. In Ambient Intelligence for Health; Springer: Cham, Switzerland, 2015; pp. 293–302.
10. Tapia, E.M.; Intille, S.S.; Larson, K. Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of the International Conference on Pervasive Computing, Vienna, Austria, 21–23 April 2004 ; Springer: Cham, Switzerland, 2004; pp. 158–175.
11. Cook, D.; Schmitter-Edgecombe, M.; Crandall, A.; Sanders, C.; Thomas, B. Collecting and disseminating smart home sensor data in the CASAS project. In Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, Boston, MA, USA, 4 – 9 April 2009; pp. 1 – 7.
12. Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing, Seoul, Korea, 21–24 September 2008; pp. 1 – 9.
13. Alshammari, N.; Alshammari, T.; Sedky, M.; Champion, J.; Bauer, C. Openshs: Open smart home simulator. Sensors 2017, 17, 1003. [CrossRef]
14. De-La-Hoz-Franco, E.; Ariza-Colpas, P.; Quero, J.M.; Espinilla, M. Sensor-based datasets for human activity recognition–A systematic review of literature. IEEE Access 2018, 6, 59192–59210. [CrossRef]
15. Rafferty, J.; Synnott, J.; Nugent, C.D.; Ennis, A.; Catherwood, P.A.; McChesney, I.; Cleland, I.; McClean, S.A Scalable, Research Oriented, Generic, Sensor Data Platform. IEEE Access 2018, 6, 45473–45484. [CrossRef]
16. Synnott, J.; Nugent, C.; Jeffers, P. Simulation of smart home activity datasets. Sensors 2015, 15, 14162–14179. [CrossRef] [PubMed]
17. Lundström, J.; Synnott, J.; Järpe, E.; Nugent, C.D. Smart home simulation using avatar control and probabilistic sampling. In Proceedings of the 2015 IEEE International Conference On Pervasive Computing And Communication Workshops (Percom Workshops), St. Louis, MO, USA, 23–27 March 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 336–341.
18. Ortiz-Barrios, M.; Lundström, J.; Synnott, J.; Järpe, E.; Sant’Anna, A. Complementing real datasets with simulated data: A regression-based approach. In Multimedia Tools and Applications; Springer: Cham, Switzerland; pp. 1–24.
19. Schreiber, T.; Schmitz, A. Surrogate time series. Phys. D Nonlinear Phenom. 2000, 142, 346–382. [CrossRef]
20. Maiwald, T.; Mammen, E.; Nandi, S.; Timmer, J. Surrogate data—A qualitative and quantitative analysis. In Mathematical Methods in Signal Processing and Digital Image Analysis; Springer: Cham, Switzerland, 2008 ; pp. 41–74.
21. Salazar, A.; Safont, G.; Vergara, L. Surrogate techniques for testing fraud detection algorithms in credit card operations. In Proceedings of the 2014 International Carnahan Conference on Security Technology ( ICCST), Rome, Italy, 13–16 October 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1 – 6.
22. Abroug, F.; Ouanes-Besbes, L.; Elatrous, S.; Brochard, L. The effect of prone positioning in acute respiratory distress syndrome or acute lung injury: A meta-analysis. Areas of uncertainty and recommendations for research. Intensive Care Med. 2008, 34, 1002. [CrossRef]
23. Synnott, J.; Chen, L.; Nugent, C.D.; Moore, G. The creation of simulated activity datasets using a graphical intelligent environment simulation tool. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 4143–4146.
24. Ariani, A.; Redmond, S.J.; Chang, D.; Lovell, N.H. Simulation of a smart home environment. In Proceedings of the 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), Bandung, Indonesia, 7–8 November 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 27–32.
25. Francillette, Y.; Boucher, E.; Bouzouane, A.; Gaboury, S. The Virtual Environment for Rapid Prototyping of the Intelligent Environment. Sensors 2017, 17, 2562. [CrossRef] [PubMed]
26. Park, B.; Min, H.; Bang, G.; Ko, I. The User Activity Reasoning Model in a Virtual Living Space Simulator. Int. J. Softw. Eng. Its Appl. 2015, 9, 53–62. [CrossRef]
27. Lee, J.W.; Cho, S.; Liu, S.; Cho, K.; Helal, S. Persim 3d: Context-driven simulation and modeling of human activities in smart spaces. IEEE Trans. Autom. Sci. Eng. 2015, 12, 1243–1256. [CrossRef]
28. McGlinn, K.; O’Neill, E.; Gibney, A.; O’Sullivan, D.; Lewis, D. SimCon: A Tool to Support Rapid Evaluation of Smart Building Application Design using Context Simulation and Virtual Reality. J. UCS 2010, 16, 1992–2018.
29. Renoux, J.; Klugl, F. Simulating daily activities in a smart home for data generation. In Proceedings of the 2018 Winter Simulation Conference (WSC), Göteborg, Sweden, 9–12 December 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 798–809.
30. Mendez-Vazquez, A.; Helal, A.; Cook, D. Simulating events to generate synthetic data for pervasive spaces. In Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research; 2009. Available online: https://pdfs.semanticscholar.org/a7ce/e34ebf272ba18eb60f1a23bd713890890e0c.pdf (accessed on 19 February 2020).
31. Cameron, A. Regression Analysis of Count Data; Cambridge University Press: Cambridge, UK, 1998.
32. Kunkler, M. Modelling negatives in stochastic reserving models. Insur. Math. Econ. 2006, 38, 540–555. [CrossRef]
33. Andersson, P.K.; Skovgaard, L.T. Regression with Linear Predictors; Springer: Cham, Switzerland, 2010. [CrossRef]
34. Joe, H.; Zhu, R. Generalized Poisson distribution: The property of mixture of Poisson and comparison with negative binomial distribution. Biom. J. 2005, 47, 219–229. [CrossRef] [PubMed]
35. Consul, P.; Famoye, F. Generalized Poisson regression-model. Commun. Stat. Theory Methods 1992, 21, 89–109. [CrossRef]
36. Marsaglia, G. Evaluating the Anderson-Darling Distribution. J. Stat. Softw. 2005, 9, 219–229. [CrossRef]
37. Ljung, G.; Box, G. On a Measure of a Lack of Fit in Time Series Models. Biometrika 1978, 65, 297–303. [CrossRef]
38. Lundström, J.; De Morais, W.O.; Menezes, M.; Gabrielli, C.; Bentes, J.; Sant’Anna, A.; Synnott, J.; Nugent, C. Halmstad intelligent home-capabilities and opportunities. In Proceedings of the International Conference on IoT Technologies for HealthCare, Västerås, Sweden, 18–19 October 2016; Springer: Cham, Switzerland, 2016; pp. 9–15.
39. Nisbet, R.; Elder, J.; Miner, G. Handbook of Statistical Analysis and Data Mining Applications; Academic Press: Cambridge, MA, USA, 2009.
40. Torrey, L.; Shavlik, J. Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques; IGI Global: Hershey, PA, USA, 2009. [CrossRef]
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spelling Ortiz Barrios, Miguel AngelCleland, IanNugent, ChrisPancardo, PabloJärpe, EricSynnott, Jonathan2020-04-13T15:08:49Z2020-04-13T15:08:49Z2020-02-282072-4292https://hdl.handle.net/11323/6174doi:10.3390/rs12050771Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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 (R2pred = 92.72%) if synthetic data is post-processed through Poisson regression incorporating dummy variables.Ortiz Barrios, Miguel Angel-will be generated-orcid-0000-0001-6890-7547-600Cleland, Ian-will be generated-orcid-0000-0003-2368-7354-600Nugent, Chris-will be generated-orcid-0000-0003-0882-7902-600Pancardo, Pablo-will be generated-orcid-0000-0002-5482-6372-600Järpe, Eric-will be generated-orcid-0000-0001-9307-9421-600Synnott, Jonathan-will be generated-orcid-0000-0002-6768-7877-600engUniversidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Activity recognitionActivities of daily living (ADL)Digital simulationPoisson regressionLarge-scale datasetsSensor systemsSmart homesSimulated data to estimate real sensor events—a poisson-regression-based modellingArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Ortiz, M.A.; López-Meza, P. Using computer simulation to improve patient flow at an outpatient internal medicine department. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence, Las Palmas de Gran Canaria, Spain, 29 November–2 December 2016; Springer: Cham, Switzerland, 2016; pp. 294–299.2. Barrios, M.A.O.; Caballero, J.E.; Sánchez, F.S. A methodology for the creation of integrated service networks in outpatient internal medicine. In Ambient Intelligence for Health; Springer: Cham, Switzerland, 2015 ; pp. 247–257.3. Cheng, L.; Nugent, C.D. Human Activity Recognition and Behaviour Analysis, 1st ed.; ; Chapter Sensor-Based Activity Recognition Review; Springer Nature: Cham, Switzerland, 2019.4. Ortiz-Barrios, M.A.; Herrera-Fontalvo, Z.; Rúa-Muñoz, J.; Ojeda-Gutiérrez, S.; De Felice, F.; Petrillo, A. An integrated approach to evaluate the risk of adverse events in hospital sector: From theory to practice. Manag. Decis. 2018, 56, 2187–2224. [CrossRef]5. Rafferty, J.; Nugent, C.D.; Liu, J.; Chen, L. From activity recognition to intention recognition for assisted living within smart homes. IEEE Trans. Hum.-Mach. Syst. 2017, 47, 368–379. [CrossRef]6. Nugent, C.; Synnott, J.; Gabrielli, C.; Zhang, S.; Espinilla, M.; Calzada, A.; Lundstrom, J.; Cleland, I.; Synnes, K.; Hallberg, J.; et al. Improving the quality of user generated data sets for activity recognition. In Ubiquitous Computing and Ambient Intelligence; Springer: Cham, Switzerland, 2016; pp. 104–110.7. Helal, S.; Kim, E.; Hossain, S. Scalable approaches to activity recognition research. In Proceedings of the 8 th International Conference Pervasive Workshop, Helsinki, Finland, 17–20 May 2010; pp. 450–453.8. Barrios, M.O.; Jiménez, H.F.; Isaza, S.N. Comparative analysis between ANP and ANP-DEMATEL for six sigma project selection process in a healthcare provider. In International Workshop on Ambient Assisted Living; Springer: Cham, Switzerland, 2014; pp. 413–416.9. Barrios, M.O.; Jiménez, H.F. Reduction of average lead time in outpatient service of obstetrics through six sigma methodology. In Ambient Intelligence for Health; Springer: Cham, Switzerland, 2015; pp. 293–302.10. Tapia, E.M.; Intille, S.S.; Larson, K. Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of the International Conference on Pervasive Computing, Vienna, Austria, 21–23 April 2004 ; Springer: Cham, Switzerland, 2004; pp. 158–175.11. Cook, D.; Schmitter-Edgecombe, M.; Crandall, A.; Sanders, C.; Thomas, B. Collecting and disseminating smart home sensor data in the CASAS project. In Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, Boston, MA, USA, 4 – 9 April 2009; pp. 1 – 7.12. Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing, Seoul, Korea, 21–24 September 2008; pp. 1 – 9.13. Alshammari, N.; Alshammari, T.; Sedky, M.; Champion, J.; Bauer, C. Openshs: Open smart home simulator. Sensors 2017, 17, 1003. [CrossRef]14. De-La-Hoz-Franco, E.; Ariza-Colpas, P.; Quero, J.M.; Espinilla, M. Sensor-based datasets for human activity recognition–A systematic review of literature. IEEE Access 2018, 6, 59192–59210. [CrossRef]15. Rafferty, J.; Synnott, J.; Nugent, C.D.; Ennis, A.; Catherwood, P.A.; McChesney, I.; Cleland, I.; McClean, S.A Scalable, Research Oriented, Generic, Sensor Data Platform. IEEE Access 2018, 6, 45473–45484. [CrossRef]16. Synnott, J.; Nugent, C.; Jeffers, P. Simulation of smart home activity datasets. Sensors 2015, 15, 14162–14179. [CrossRef] [PubMed]17. Lundström, J.; Synnott, J.; Järpe, E.; Nugent, C.D. Smart home simulation using avatar control and probabilistic sampling. In Proceedings of the 2015 IEEE International Conference On Pervasive Computing And Communication Workshops (Percom Workshops), St. Louis, MO, USA, 23–27 March 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 336–341.18. Ortiz-Barrios, M.; Lundström, J.; Synnott, J.; Järpe, E.; Sant’Anna, A. Complementing real datasets with simulated data: A regression-based approach. In Multimedia Tools and Applications; Springer: Cham, Switzerland; pp. 1–24.19. Schreiber, T.; Schmitz, A. Surrogate time series. Phys. D Nonlinear Phenom. 2000, 142, 346–382. [CrossRef]20. Maiwald, T.; Mammen, E.; Nandi, S.; Timmer, J. Surrogate data—A qualitative and quantitative analysis. In Mathematical Methods in Signal Processing and Digital Image Analysis; Springer: Cham, Switzerland, 2008 ; pp. 41–74.21. Salazar, A.; Safont, G.; Vergara, L. Surrogate techniques for testing fraud detection algorithms in credit card operations. In Proceedings of the 2014 International Carnahan Conference on Security Technology ( ICCST), Rome, Italy, 13–16 October 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1 – 6.22. Abroug, F.; Ouanes-Besbes, L.; Elatrous, S.; Brochard, L. The effect of prone positioning in acute respiratory distress syndrome or acute lung injury: A meta-analysis. Areas of uncertainty and recommendations for research. Intensive Care Med. 2008, 34, 1002. [CrossRef]23. Synnott, J.; Chen, L.; Nugent, C.D.; Moore, G. The creation of simulated activity datasets using a graphical intelligent environment simulation tool. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 4143–4146.24. Ariani, A.; Redmond, S.J.; Chang, D.; Lovell, N.H. Simulation of a smart home environment. In Proceedings of the 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), Bandung, Indonesia, 7–8 November 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 27–32.25. Francillette, Y.; Boucher, E.; Bouzouane, A.; Gaboury, S. The Virtual Environment for Rapid Prototyping of the Intelligent Environment. Sensors 2017, 17, 2562. [CrossRef] [PubMed]26. Park, B.; Min, H.; Bang, G.; Ko, I. The User Activity Reasoning Model in a Virtual Living Space Simulator. Int. J. Softw. Eng. Its Appl. 2015, 9, 53–62. [CrossRef]27. Lee, J.W.; Cho, S.; Liu, S.; Cho, K.; Helal, S. Persim 3d: Context-driven simulation and modeling of human activities in smart spaces. IEEE Trans. Autom. Sci. Eng. 2015, 12, 1243–1256. [CrossRef]28. McGlinn, K.; O’Neill, E.; Gibney, A.; O’Sullivan, D.; Lewis, D. SimCon: A Tool to Support Rapid Evaluation of Smart Building Application Design using Context Simulation and Virtual Reality. J. UCS 2010, 16, 1992–2018.29. Renoux, J.; Klugl, F. Simulating daily activities in a smart home for data generation. In Proceedings of the 2018 Winter Simulation Conference (WSC), Göteborg, Sweden, 9–12 December 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 798–809.30. Mendez-Vazquez, A.; Helal, A.; Cook, D. Simulating events to generate synthetic data for pervasive spaces. In Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research; 2009. Available online: https://pdfs.semanticscholar.org/a7ce/e34ebf272ba18eb60f1a23bd713890890e0c.pdf (accessed on 19 February 2020).31. Cameron, A. Regression Analysis of Count Data; Cambridge University Press: Cambridge, UK, 1998.32. Kunkler, M. Modelling negatives in stochastic reserving models. Insur. Math. Econ. 2006, 38, 540–555. [CrossRef]33. Andersson, P.K.; Skovgaard, L.T. Regression with Linear Predictors; Springer: Cham, Switzerland, 2010. [CrossRef]34. Joe, H.; Zhu, R. 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[CrossRef]PublicationORIGINALSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdfSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdfapplication/pdf1035431https://repositorio.cuc.edu.co/bitstreams/b8ce90a4-60ad-4a8e-9737-c8d532523e5b/download0ca75d0650027ab735c2c59e38e647cdMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/2cdf301e-2aa5-441e-93af-5b5ed9ddbfec/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/07fd7a95-eb53-452b-bd68-352bf4b2d5e9/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdf.jpgSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdf.jpgimage/jpeg67057https://repositorio.cuc.edu.co/bitstreams/0ccd374c-6597-40ab-817f-9bd3e209b209/download049c2cef53a8fe2fb9afe97d9f043612MD54TEXTSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdf.txtSimulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling.pdf.txttext/plain60896https://repositorio.cuc.edu.co/bitstreams/7085f228-5af7-46f6-8af9-260784f08b97/download2d139936f06652f5bfb9e3bbe36692b7MD5511323/6174oai:repositorio.cuc.edu.co:11323/61742024-09-17 10:17:47.255http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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