An Improved Ensemble Machine Learning Algorithm for Wearable Sensor Data Based Human Activity Recognition

Huu Du Nguyen (), Kim Phuc Tran (), Xianyi Zeng (), Ludovic Koehl () and Guillaume Tartare ()
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Huu Du Nguyen: Dong A University
Kim Phuc Tran: ENSAIT & GEMTEX
Xianyi Zeng: ENSAIT & GEMTEX
Ludovic Koehl: Ecole Nationale Supérieure des Arts et Industries Textiles, GEMTEX Laboratory
Guillaume Tartare: Ecole Nationale Supérieure des Arts et Industries Textiles, GEMTEX Laboratory

A chapter in Reliability and Statistical Computing, 2020, pp 207-228 from Springer

Abstract: Abstract Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this chapter, we propose an improved machine learning method based on the ensemble algorithm to boost the performance of these machine learning methods for HAR.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-030-43412-0_13

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DOI: 10.1007/978-3-030-43412-0_13

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