Robust sensor-based human activity recognition with snippet consensus neural networks

Yu Huang, Meng Chieh Lee, Vincent S. Tseng*, Ching Jui Hsiao, Chi Chiang Huang

*此作品的通信作者

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538674772
DOIs
出版狀態Published - 5月 2019
事件16th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019 - Chicago, United States
持續時間: 19 5月 201922 5月 2019

出版系列

名字2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019 - Proceedings

Conference

Conference16th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019
國家/地區United States
城市Chicago
期間19/05/1922/05/19

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