# Active Learning for WBAN-based Health Monitoring

arXiv: https://arxiv.org/abs/2408.02849 · HTML: https://mlsanity.com/paper/2408.02849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers](https://mlsanity.com/paper/2107.05687/) (2021)
- [Robust Coreset Construction for Distributed Machine Learning](https://mlsanity.com/paper/1904.05961/) (2019)

## Built on by (most cited first, 2 of 2)

- [Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise](https://mlsanity.com/paper/2508.15523/) (2025)
- [SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition](https://mlsanity.com/paper/2605.04608/) (2026)

## Abstract (arXiv)

We consider a novel active learning problem motivated by the need of learning machine learning models for health monitoring in wireless body area network (WBAN). Due to the limited resources at body sensors, collecting each unlabeled sample in WBAN incurs a nontrivial cost. Moreover, training health monitoring models typically requires labels indicating the patient's health state that need to be generated by healthcare professionals, which cannot be obtained at the same pace as data collection. These challenges make our problem fundamentally different from classical active learning, where unlabeled samples are free and labels can be queried in real time. To handle these challenges, we propose a two-phased active learning method, consisting of an online phase where a coreset construction algorithm is proposed to select a subset of unlabeled samples based on their noisy predictions, and an offline phase where the selected samples are labeled to train the target model. The samples selected by our algorithm are proved to yield a guaranteed error in approximating the full dataset in evaluating the loss function. Our evaluation based on real health monitoring data and our own experimentation demonstrates that our solution can drastically save the data curation cost without sacrificing the quality of the target model.