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2021 | OriginalPaper | Buchkapitel

Determination of the Most Relevant Features to Improve the Performance of RF Classifier in Human Activity Recognition

verfasst von : Geovanna Jiménez-Gómez, Daniela Navarro-Escorcia, Dionicio Neira-Rodado, Ian Cleland

Erschienen in: Computer Information Systems and Industrial Management

Verlag: Springer International Publishing

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Abstract

The 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.

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Dementia: https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed 15 May 2021

Prince, 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. 87

Prince, 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 Future

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 6, 59192–59210 (2018). https://doi.org/10.1109/ACCESS.2018.2873502CrossRef

Aparisi, 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)

Noor, 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://doi.org/10.1016/j.pmcj.2016.09.009CrossRef

Cerasuolo, J.O., et al.: Population-based stroke and dementia incidence trends: age and sex variations. Alzheimers Dement. 13(10), 1081–1088 (2017). https://doi.org/10.1016/j.jalz.2017.02.010CrossRef

Neira-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://doi.org/10.3390/s20071858CrossRef

Ni, 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://doi.org/10.3390/s150511312CrossRef

10.

Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015). https://doi.org/10.1109/JSEN.2014.2370945CrossRefMATH

11.

Chen, L., Hoey, J., Chris, N., Cook, D., Yu, Z.: Sensor-based activity recognition. IEEE Trans. 42(6), 790–808 (2012)

12.

Kleinberger, 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://doi.org/10.1007/978-3-540-73281-5_11CrossRef

13.

Chen, 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://doi.org/10.1109/SMC.2015.263

14.

Qi, 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://doi.org/10.3390/s19173731CrossRef

15.

Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012). https://doi.org/10.1145/2347736.2347755CrossRef

16.

Pires, 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://doi.org/10.3390/s16020184CrossRef

17.

Veeriah, V., Zhuang, N., Qi, G.-J.: Differential recurrent neural networks for action recognition (2015)

18.

Janidarmian, 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://doi.org/10.3390/s17030529CrossRef

19.

Tian, 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://doi.org/10.1080/21642583.2020.1723142CrossRef

20.

Li, 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://doi.org/10.3390/s18020679CrossRef

21.

Irvine, N.: The Impact of Dataset Quality on the Performance of Data-Driven Approaches for Human Activity Recognition, pp. 1–8

22.

Cornacchia, 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://doi.org/10.1109/JSEN.2016.2628346CrossRef

23.

Koziarski, M., Krawczyk, B., Woźniak, M.: The deterministic subspace method for constructing classifier ensembles. Pattern Anal. Appl. 20(4), 981–990 (2017). https://doi.org/10.1007/s10044-017-0655-2MathSciNetCrossRef

Titel: Determination of the Most Relevant Features to Improve the Performance of RF Classifier in Human Activity Recognition
verfasst von: Geovanna Jiménez-Gómez
Daniela Navarro-Escorcia
Dionicio Neira-Rodado
Ian Cleland
Verlag: Springer International Publishing
Buch: Computer Information Systems and Industrial Management
Print ISBN: 978-3-030-84339-7

Electronic ISBN: 978-3-030-84340-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-84340-3_3

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