Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Abstract
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm. We develop a context-aware mixture of deep models termed the $\alpha$-$\beta$ network coupled with uncertainty quantification (UQ) based upon maximum entropy to enhance human activity recognition performance. We improve accuracy and F score by 10% by identifying high-level contexts in a data-driven way to guide model development. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets, demonstrating improved accuracy through unknown context discovery.
Cite
Text
Huo et al. "Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery." Artificial Intelligence and Statistics, 2020.Markdown
[Huo et al. "Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery." Artificial Intelligence and Statistics, 2020.](https://mlanthology.org/aistats/2020/huo2020aistats-uncertainty/)BibTeX
@inproceedings{huo2020aistats-uncertainty,
title = {{Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery}},
author = {Huo, Zepeng and PakBin, Arash and Chen, Xiaohan and Hurley, Nathan and Yuan, Ye and Qian, Xiaoning and Wang, Zhangyang and Huang, Shuai and Mortazavi, Bobak},
booktitle = {Artificial Intelligence and Statistics},
year = {2020},
pages = {3894-3904},
volume = {108},
url = {https://mlanthology.org/aistats/2020/huo2020aistats-uncertainty/}
}