Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

• URL: http://arxiv.org/abs/2003.01753v1
• Date: Tue, 3 Mar 2020 19:35:34 GMT
• Title: Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
• Authors: Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi
• Abstract summary: We develop a context-aware mixture of deep models termed the alpha-beta network. We improve accuracy and F score by 10% by identifying high-level contexts. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets.
• Score: 85.36948722680822
• License: http://creativecommons.org/licenses/by/4.0/
• 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}-\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.

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