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

arXiv: https://arxiv.org/abs/2003.01753 · HTML: https://mlsanity.com/paper/2003.01753/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions](https://mlsanity.com/paper/2110.03260/) (2021)
- [Feature Hallucination for Self-supervised Action Recognition](https://mlsanity.com/paper/2506.20342/) (2025)
- [Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data](https://mlsanity.com/paper/2207.11382/) (2022)
- [Conformal Prediction in Multi-User Settings: An Evaluation](https://mlsanity.com/paper/2312.05195/) (2023)
- [Description of Structural Biases and Associated Data in Sensor-Rich Environments](https://mlsanity.com/paper/2104.04885/) (2021)
- [DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness](https://mlsanity.com/paper/2209.15415/) (2022)
- [Vision: "What Don't I Know About User's Preferences?" How Uncertainty Estimation Improves Proactive Assistant Alignment](https://mlsanity.com/paper/2610.15948/) (2026)

## Abstract (arXiv)

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 α-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.