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Proceedings Paper

Human motion classification based on inertial sensors with extreme gradient boosting
Author(s): Yue Zhang; Zhiqiang Peng
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Paper Abstract

In this paper, we apply Extreme Gradient Boosting (XGBoost) widely used in many areas to human motion classification. During this research, we compare the performance of XGBoost and other machine learning methods, such as Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (k-NN). In addition, we make a comprehensive comparison of XGBoost and Random Forest (RF). The experimental results reveal that XGBoost can achieve better results in activity classification based on inertial sensors.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361R (29 October 2018); doi: 10.1117/12.2514563
Show Author Affiliations
Yue Zhang, Graduate School at Shenzhen, Tsinghua Univ. (China)
Zhiqiang Peng, Graduate School at Shenzhen, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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