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dc.creatorOrtiz Barrios, Miguel Angel
dc.creatorCleland, Ian
dc.creatorNugent, Chris
dc.creatorPancardo, Pablo
dc.creatorJärpe, Eric
dc.creatorSynnott, Jonathan
dc.description.abstractAutomatic 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
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.subjectActivity recognitionspa
dc.subjectActivities of daily living (ADL)spa
dc.subjectDigital simulationspa
dc.subjectPoisson regressionspa
dc.subjectLarge-scale datasetsspa
dc.subjectSensor systemsspa
dc.subjectSmart homesspa
dc.titleSimulated data to estimate real sensor events—a poisson-regression-based modellingspa
dcterms.references1. 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–
dcterms.references2. 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–
dcterms.references3. Cheng, L.; Nugent, C.D. Human Activity Recognition and Behaviour Analysis, 1st ed.; ; Chapter Sensor-Based Activity Recognition Review; Springer Nature: Cham, Switzerland,
dcterms.references4. 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]spa
dcterms.references5. 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]spa
dcterms.references6. 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–
dcterms.references7. 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–
dcterms.references8. 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–
dcterms.references9. 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–
dcterms.references10. 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–
dcterms.references11. 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 –
dcterms.references12. 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 –
dcterms.references13. Alshammari, N.; Alshammari, T.; Sedky, M.; Champion, J.; Bauer, C. Openshs: Open smart home simulator. Sensors 2017, 17, 1003. [CrossRef]spa
dcterms.references14. 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]spa
dcterms.references15. 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]spa
dcterms.references16. Synnott, J.; Nugent, C.; Jeffers, P. Simulation of smart home activity datasets. Sensors 2015, 15, 14162–14179. [CrossRef] [PubMed]spa
dcterms.references17. 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–
dcterms.references18. 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–
dcterms.references19. Schreiber, T.; Schmitz, A. Surrogate time series. Phys. D Nonlinear Phenom. 2000, 142, 346–382. [CrossRef]spa
dcterms.references20. 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–
dcterms.references21. 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 –
dcterms.references22. 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]spa
dcterms.references23. 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–
dcterms.references24. 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–
dcterms.references25. Francillette, Y.; Boucher, E.; Bouzouane, A.; Gaboury, S. The Virtual Environment for Rapid Prototyping of the Intelligent Environment. Sensors 2017, 17, 2562. [CrossRef] [PubMed]spa
dcterms.references26. 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]spa
dcterms.references27. 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]spa
dcterms.references28. 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–
dcterms.references29. 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–
dcterms.references30. 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: (accessed on 19 February 2020).spa
dcterms.references31. Cameron, A. Regression Analysis of Count Data; Cambridge University Press: Cambridge, UK,
dcterms.references32. Kunkler, M. Modelling negatives in stochastic reserving models. Insur. Math. Econ. 2006, 38, 540–555. [CrossRef]spa
dcterms.references33. Andersson, P.K.; Skovgaard, L.T. Regression with Linear Predictors; Springer: Cham, Switzerland, 2010. [CrossRef]spa
dcterms.references34. 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]spa
dcterms.references35. Consul, P.; Famoye, F. Generalized Poisson regression-model. Commun. Stat. Theory Methods 1992, 21, 89–109. [CrossRef]spa
dcterms.references36. Marsaglia, G. Evaluating the Anderson-Darling Distribution. J. Stat. Softw. 2005, 9, 219–229. [CrossRef]spa
dcterms.references37. Ljung, G.; Box, G. On a Measure of a Lack of Fit in Time Series Models. Biometrika 1978, 65, 297–303. [CrossRef]spa
dcterms.references38. 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–
dcterms.references39. Nisbet, R.; Elder, J.; Miner, G. Handbook of Statistical Analysis and Data Mining Applications; Academic Press: Cambridge, MA, USA,
dcterms.references40. 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]spa

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