  Deep learning models have shown great promise in various healthcare
applications. However, most models are developed and validated on small-scale
datasets, as collecting high-quality (gold-standard) labels for health
applications is often costly and time-consuming. As a result, these models may
suffer from overfitting and not generalize well to unseen data. At the same
time, an extensive amount of data with imprecise labels (silver-standard) is
starting to be generally available, as collected from inexpensive wearables
like accelerometers and electrocardiography sensors. These currently
underutilized datasets and labels can be leveraged to produce more accurate
clinical models. In this work, we propose UDAMA, a novel model with two key
components: Unsupervised Domain Adaptation and Multi-discriminator Adversarial
training, which leverage noisy data from source domain (the silver-standard
dataset) to improve gold-standard modeling. We validate our framework on the
challenging task of predicting lab-measured maximal oxygen consumption
(VO$_{2}$max), the benchmark metric of cardio-respiratory fitness, using
free-living wearable sensor data from two cohort studies as inputs. Our
experiments show that the proposed framework achieves the best performance of
corr = 0.665 $\pm$ 0.04, paving the way for accurate fitness estimation at
scale.
