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

TL;DR

This paper introduces a deep, context-aware activity recognition model with uncertainty quantification, improving accuracy and enabling discovery of unknown contexts in wearable computing.

Contribution

It proposes the {ta} network with maximum entropy-based uncertainty quantification, enhancing adaptability and accuracy in recognizing human activities across varying and unknown contexts.

Findings

Improved accuracy and F score by 10%

Effective identification of high-level contexts

Enhanced 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}-\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.