  Freezing of gait (FoG) is one of the most common symptoms of Parkinson's
disease, which is a neurodegenerative disorder of the central nervous system
impacting millions of people around the world. To address the pressing need to
improve the quality of treatment for FoG, devising a computer-aided detection
and quantification tool for FoG has been increasingly important. As a
non-invasive technique for collecting motion patterns, the footstep pressure
sequences obtained from pressure sensitive gait mats provide a great
opportunity for evaluating FoG in the clinic and potentially in the home
environment. In this study, FoG detection is formulated as a sequential
modelling task and a novel deep learning architecture, namely Adversarial
Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across
multiple levels. A novel adversarial training scheme is introduced with a
multi-level subject discriminator to obtain subject-independent FoG
representations, which helps to reduce the over-fitting risk due to the high
inter-subject variance. As a result, robust FoG detection can be achieved for
unseen subjects. The proposed scheme also sheds light on improving
subject-level clinical studies from other scenarios as it can be integrated
with many existing deep architectures. To the best of our knowledge, this is
one of the first studies of footstep pressure-based FoG detection and the
approach of utilizing ASTN is the first deep neural network architecture in
pursuit of subject-independent representations. Experimental results on 393
trials collected from 21 subjects demonstrate encouraging performance of the
proposed ASTN for FoG detection with an AUC 0.85.
