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Determination of the most relevant features to improve the performance of RF classifier in human activity recognition

dc.contributor.authorJiménez-Gómez, Geovannaspa
dc.contributor.authorNavarro-Escorcia, Danielaspa
dc.contributor.authorNeira Rodado, Dioniciospa
dc.contributor.authorCleland, Ianspa
dc.date.accessioned2021-11-08T13:11:42Z
dc.date.available2021-11-08T13:11:42Z
dc.date.issued2021-09-17
dc.identifier.issn978-303084339-7spa
dc.identifier.urihttps://hdl.handle.net/11323/8843spa
dc.description.abstractThe impact that neurodegenerative diseases have in our society, have made human activity recognition (HAR) arise as a relevant field of study. The quality of life of people with such conditions, can be significantly improved with the outcomes of the projects within this area. The application of machine learning techniques on data from low level sensors such as accelerometers is the base of HAR. To improve the performance of these classifiers, it is necessary to carry out an adequate training process. To improve the training process, an analysis of the different features used in literature to tackle these problems was performed on datasets constructed with students performing 18 different activities of daily living. The outcome of the process shows that an adequate selection of features improves the performance of Random Forest from 94.6% to 97.2%. It was also found that 78 features explain 80% of the variability.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherSpringer International Publishingspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceLecture Notes in Computer Sciencespa
dc.subjectHARspa
dc.subjectMachine learningspa
dc.subjectFeature selectionspa
dc.subjectRF classifierspa
dc.titleDetermination of the most relevant features to improve the performance of RF classifier in human activity recognitionspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.springerprofessional.de/en/determination-of-the-most-relevant-features-to-improve-the-perfo/19669904spa
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccessspa
dc.identifier.doi10.1007/978-3-030-84340-3_3spa
dc.date.embargoEnd2022-09-17
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.referencesDementia: https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed 15 May 2021spa
dc.relation.referencesPrince, M., Wimo, A., Guerchet, M., Ali, G.-C., Wu, Y.-T., Prina, M.: World Alzheimer Report 2015, The Global Impact of Dementia: An Analysis of Prevalence, Incidence, Cost and Trends, p. 87spa
dc.relation.referencesPrince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., Karagiannidou, M.: World Alzheimer Report 2016 Improving Healthcare for People Living with Dementia Coverage, QualIty and Costs Now and in the Futurespa
dc.relation.referencesDe-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 6, 59192–59210 (2018). https://ezproxy.cuc.edu.co:2067/10.1109/ACCESS.2018.2873502spa
dc.relation.referencesAparisi, F., Carlos, J., Díaz, G.: Aumento de la potencia del gráfico de control multivariante T 2 de Hotelling utilizando señales adicionales de falta de control (2001)spa
dc.relation.referencesNoor, M.H.M., Salcic, Z., Wang, K.I.K.: Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Perv. Mob. Comput. 38, 41–59 (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.pmcj.2016.09.009spa
dc.relation.referencesCerasuolo, J.O., et al.: Population-based stroke and dementia incidence trends: age and sex variations. Alzheimers Dement. 13(10), 1081–1088 (2017). https://ezproxy.cuc.edu.co:2067/10.1016/j.jalz.2017.02.010spa
dc.relation.referencesNeira-Rodado, D., Nugent, C., Cleland, I., Velasquez, J., Viloria, A.: Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition. Sensors 20(7), 2020 (1858). https://ezproxy.cuc.edu.co:2067/10.3390/s20071858spa
dc.relation.referencesNi, Q., García Hernando, A., de la Cruz, I.: The elderly’s independent living in smart homes: a characterization of activities and sensing infrastructure survey to facilitate services development. Sensors 15(5), 11312–11362 (2015). https://ezproxy.cuc.edu.co:2067/10.3390/s150511312spa
dc.relation.referencesMukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015). https://ezproxy.cuc.edu.co:2067/10.1109/JSEN.2014.2370945spa
dc.relation.referencesChen, L., Hoey, J., Chris, N., Cook, D., Yu, Z.: Sensor-based activity recognition. IEEE Trans. 42(6), 790–808 (2012)spa
dc.relation.referencesKleinberger, T., Becker, M., Ras, E., Holzinger, A., Müller, P.: Ambient intelligence in assisted living: enable elderly people to handle future interfaces. In: Stephanidis, Constantine (ed.) UAHCI 2007. LNCS, vol. 4555, pp. 103–112. Springer, Heidelberg (2007). https://ezproxy.cuc.edu.co:2067/10.1007/978-3-540-73281-5_11spa
dc.relation.referencesChen, Y., Xue, Y.: A deep learning approach to human activity recognition based on single accelerometer. In: Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, pp. 1488–1492 (2016). https://ezproxy.cuc.edu.co:2067/10.1109/SMC.2015.263spa
dc.relation.referencesQi, W., Su, H., Yang, C., Ferrigno, G., De Momi, E., Aliverti, A.: A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone. Sensors (Switzerland) 19(17), 3731 (2019). https://ezproxy.cuc.edu.co:2067/10.3390/s19173731spa
dc.relation.referencesDomingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012). https://ezproxy.cuc.edu.co:2067/10.1145/2347736.2347755spa
dc.relation.referencesPires, I., et al.: From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors 16(2), 184 (2016). https://ezproxy.cuc.edu.co:2067/10.3390/s16020184spa
dc.relation.referencesVeeriah, V., Zhuang, N., Qi, G.-J.: Differential recurrent neural networks for action recognition (2015)spa
dc.relation.referencesJanidarmian, M., Roshan Fekr, A., Radecka, K., Zilic, Z.: A comprehensive analysis on wearable acceleration sensors in human activity recognition. Sensors 17(3), 529 (2017). https://ezproxy.cuc.edu.co:2067/10.3390/s17030529spa
dc.relation.referencesTian, Y., Zhang, J., Wang, J., Geng, Y., Wang, X.: Robust human activity recognition using single accelerometer via wavelet energy spectrum features and ensemble feature selection. Syst. Sci. Contr. Eng. 8(1), 83–96 (2020). https://ezproxy.cuc.edu.co:2067/10.1080/21642583.2020.1723142spa
dc.relation.referencesLi, F., Shirahama, K., Nisar, M.A., Köping, L., Grzegorzek, M.: Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors 18(3), 679 (2018). https://ezproxy.cuc.edu.co:2067/10.3390/s18020679spa
dc.relation.referencesIrvine, N.: The Impact of Dataset Quality on the Performance of Data-Driven Approaches for Human Activity Recognition, pp. 1–8spa
dc.relation.referencesCornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2017). https://ezproxy.cuc.edu.co:2067/10.1109/JSEN.2016.2628346spa
dc.relation.referencesKoziarski, M., Krawczyk, B., Woźniak, M.: The deterministic subspace method for constructing classifier ensembles. Pattern Anal. Appl. 20(4), 981–990 (2017). https://ezproxy.cuc.edu.co:2067/10.1007/s10044-017-0655-2spa
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dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
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dc.rights.coarhttp://purl.org/coar/access_right/c_f1cfspa


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