  Data-driven approaches for remote detection of Parkinson's Disease and its
motor symptoms have proliferated in recent years, owing to the potential
clinical benefits of early diagnosis. The holy grail of such approaches is the
free-living scenario, in which data are collected continuously and
unobtrusively during every day life. However, obtaining fine-grained
ground-truth and remaining unobtrusive is a contradiction and therefore, the
problem is usually addressed via multiple-instance learning. Yet for large
scale studies, obtaining even the necessary coarse ground-truth is not trivial,
as a complete neurological evaluation is required. In contrast, large scale
collection of data without any ground-truth is much easier. Nevertheless,
utilizing unlabelled data in a multiple-instance setting is not
straightforward, as the topic has received very little research attention. Here
we try to fill this gap by introducing a new method for combining
semi-supervised with multiple-instance learning. Our approach builds on the
Virtual Adversarial Training principle, a state-of-the-art approach for regular
semi-supervised learning, which we adapt and modify appropriately for the
multiple-instance setting. We first establish the validity of the proposed
approach through proof-of-concept experiments on synthetic problems generated
from two well-known benchmark datasets. We then move on to the actual task of
detecting PD tremor from hand acceleration signals collected in-the-wild, but
in the presence of additional completely unlabelled data. We show that by
leveraging the unlabelled data of 454 subjects we can achieve large performance
gains (up to 9% increase in F1-score) in per-subject tremor detection for a
cohort of 45 subjects with known tremor ground-truth.
