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}-\b{eta} 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.
@article{arxiv.2003.01753,
title = {Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery},
author = {Zepeng Huo and Arash PakBin and Xiaohan Chen and Nathan Hurley and Ye Yuan and Xiaoning Qian and Zhangyang Wang and Shuai Huang and Bobak Mortazavi},
journal= {arXiv preprint arXiv:2003.01753},
year = {2020}
}