Facial Tic Detection in Untrimmed Videos of Tourette Syndrome Patients
This addresses the limited availability of therapists and in-home follow-up for Tourette Syndrome patients, though it is incremental as it builds on existing detection methods with a focus on interpretability.
The paper tackled the problem of automatically detecting facial tics in untrimmed videos of Tourette Syndrome patients to support behavioral therapy, achieving comparable performance to state-of-the-art systems in terms of average precision.
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.