  Tourette Syndrome (TS) is a behavior disorder that onsets in childhood and is
characterized by the expression of involuntary movements and sounds commonly
referred to as tics. Behavioral therapy is the first-line treatment for
patients with TS, and it helps patients raise awareness about tic occurrence as
well as develop tic inhibition strategies. However, the limited availability of
therapists and the difficulties for in-home follow up work limits its
effectiveness. An automatic tic detection system that is easy to deploy could
alleviate the difficulties of home-therapy by providing feedback to the
patients while exercising tic awareness. In this work, we propose a novel
architecture (T-Net) for automatic tic detection and classification from
untrimmed videos. T-Net combines temporal detection and segmentation and
operates on features that are interpretable to a clinician. We compare T-Net to
several state-of-the-art systems working on deep features extracted from the
raw videos and T-Net achieves comparable performance in terms of average
precision while relying on interpretable features needed in clinical practice.
