  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.
