Wearable data from subjects playing Super Mario, sitting university
  exams, or performing physical exercise help detect acute mood episodes via
  self-supervised learning
Filippo Corponi Bryan M. Li Gerard Anmella Clàudia Valenzuela-Pascual Ariadna Mas Isabella Pacchiarotti Marc Valentí Iria Grande Antonio Benabarre Marina Garriga Eduard Vieta Allan H Young Stephen M. Lawrie Heather C. Whalley Diego Hidalgo-Mazzei Antonio Vergari
Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising
paradigm to monitor mood disorders (MDs), a major determinant of worldwide
disease burden. However, collecting and annotating wearable data is very
resource-intensive. Studies of this kind can thus typically afford to recruit
only a couple dozens of patients. This constitutes one of the major obstacles
to applying modern supervised machine learning techniques to MDs detection. In
this paper, we overcome this data bottleneck and advance the detection of MDs
acute episode vs stable state from wearables data on the back of recent
advances in self-supervised learning (SSL). This leverages unlabelled data to
learn representations during pre-training, subsequently exploited for a
supervised task. First, we collected open-access datasets recording with an
Empatica E4 spanning different, unrelated to MD monitoring, personal sensing
tasks -- from emotion recognition in Super Mario players to stress detection in
undergraduates -- and devised a pre-processing pipeline performing on-/off-body
detection, sleep-wake detection, segmentation, and (optionally) feature
extraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the
largest to date open access collection, and its pre-processing pipeline.
Second, we show that SSL confidently outperforms fully-supervised pipelines
using either our novel E4-tailored Transformer architecture (E4mer) or
classical baseline XGBoost: 81.23% against 75.35% (E4mer) and 72.02% (XGBoost)
correctly classified recording segments from 64 (half acute, half stable)
patients. Lastly, we illustrate that SSL performance is strongly associated
with the specific surrogate task employed for pre-training as well as with
unlabelled data availability.