Wearable Sensor Data Based Human Activity Recognition Using Machine Learning: A New Approach  
Author H. D. Nguyen

 
Co-Author(s) K. P. Tran; X. Zeng; L. Koehl; G. Tartare

 
Abstract Recent years have witnessed the rapid development of human activity recognition (HAR) based on werable 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 paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR.

 
Keywords Human Activity Recognition, Wearable sensor, Ensemble learning method, Machine learning.
   
    Article #:  DSBFI19-6
 
Proceedings of ISSAT International Conference on Data Science in Business, Finance and Industry
July 3-5, 2019 - Da Nang, Vietnam