  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that
affects about 1% of the population above 60 years old, causing symptoms that
are subtle at first, but whose intensity increases as the disease progresses.
Automated detection of these symptoms could offer clues as to the early onset
of the disease, thus improving the expected clinical outcomes of the patients
via appropriately targeted interventions. This potential has led many
researchers to develop methods that use widely available sensors to measure and
quantify the presence of PD symptoms such as tremor, rigidity and braykinesia.
However, most of these approaches operate under controlled settings, such as in
lab or at home, thus limiting their applicability under free-living conditions.
In this work, we present a method for automatically identifying tremorous
episodes related to PD, based on IMU signals captured via a smartphone device.
We propose a Multiple-Instance Learning approach, wherein a subject is
represented as an unordered bag of accelerometer signal segments and a single,
expert-provided, tremor annotation. Our method combines deep feature learning
with a learnable pooling stage that is able to identify key instances within
the subject bag, while still being trainable end-to-end. We validate our
algorithm on a newly introduced dataset of 45 subjects, containing
accelerometer signals collected entirely in-the-wild. The good classification
performance obtained in the conducted experiments suggests that the proposed
method can efficiently navigate the noisy environment of in-the-wild
recordings.
