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

In: Reliability and Statistical Computing

Author

Listed:
  • 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)

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.

Suggested Citation

  • Huu Du Nguyen & Kim Phuc Tran & Xianyi Zeng & Ludovic Koehl & Guillaume Tartare, 2020. "An Improved Ensemble Machine Learning Algorithm for Wearable Sensor Data Based Human Activity Recognition," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Reliability and Statistical Computing, pages 207-228, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-43412-0_13
    DOI: 10.1007/978-3-030-43412-0_13
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