Facial Tic Detection in Untrimmed Videos of Tourette Syndrome Patients

TL;DR

This paper introduces T-Net, a novel architecture for automatic detection and classification of tics in untrimmed videos of Tourette Syndrome patients, aiming to support behavioral therapy with interpretable features.

Contribution

The paper presents T-Net, a new model that combines temporal detection and segmentation for tic recognition using clinically interpretable features.

Findings

T-Net achieves comparable average precision to state-of-the-art systems.

T-Net operates on interpretable features suitable for clinical use.

The system facilitates in-home tic monitoring and therapy support.

Abstract

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.