  The emergence of digital technologies such as smartphones in healthcare
applications have demonstrated the possibility of developing rich, continuous,
and objective measures of multiple sclerosis (MS) disability that can be
administered remotely and out-of-clinic. In this work, deep convolutional
neural networks (DCNN) applied to smartphone inertial sensor data were shown to
better distinguish healthy from MS participant ambulation, compared to standard
Support Vector Machine (SVM) feature-based methodologies. To overcome the
typical limitations associated with remotely generated health data, such as low
subject numbers, sparsity, and heterogeneous data, a transfer learning (TL)
model from similar large open-source datasets was proposed. Our TL framework
utilised the ambulatory information learned on Human Activity Recognition (HAR)
tasks collected from similar smartphone-based sensor data. A lack of
transparency of "black-box" deep networks remains one of the largest stumbling
blocks to the wider acceptance of deep learning for clinical applications.
Ensuing work therefore aimed to visualise DCNN decisions attributed by
relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the
LRP framework, the patterns captured from smartphone-based inertial sensor data
that were reflective of those who are healthy versus persons with MS (PwMS)
could begin to be established and understood. Interpretations suggested that
cadence-based measures, gait speed, and ambulation-related signal perturbations
were distinct characteristics that distinguished MS disability from healthy
participants. Robust and interpretable outcomes, generated from high-frequency
out-of-clinic assessments, could greatly augment the current in-clinic
assessment picture for PwMS, to inform better disease management techniques,
and enable the development of better therapeutic interventions.
