標題: | Robust Sensor-based Human Activity Recognition with Snippet Consensus Neural Networks |
作者: | Huang, Yu Lee, Meng-Chieh Tseng, Vincent S. Hsiao, Ching-Jui Huang, Chi-Chiang 資訊工程學系 Department of Computer Science |
公開日期: | 1-一月-2019 |
摘要: | Sensor-based human activity recognition is an important problem in pervasive computing, which has attracted lots of attention from the research community in the past few years. The existing relevant studies focused on using handcrafted features or machine learning-based methods to tackle this problem. However, these methods are usually limited to specific datasets, such that the generality is limited. Some methods are also limited to strict experimental environments, which do not take stability into consideration. In this paper, we propose a robust and novel deep learning-based framework, named Snippet Consensus Neural Networks (SCNet), which aims to conquer these challenges. Through a series of experiments, the proposed framework is verified to outperform seven state-of-the-art methods on five datasets in terms of not only accuracy but also generality and stability, averagely improving 10% on mean accuracy. |
URI: | http://hdl.handle.net/11536/153274 |
ISBN: | 978-1-5386-7477-2 |
ISSN: | 2376-8886 |
期刊: | 2019 IEEE 16TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN) |
起始頁: | 0 |
結束頁: | 0 |
顯示於類別: | 會議論文 |