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.

翻译：Tourette综合症(TS)是一种在童年就开始出现的行为障碍,其特征是非自愿运动的表现和声音通常被称为Tics。行为疗法是TS病人的第一线治疗,它帮助病人提高对发病情况的认识,并制订抑制性战略。然而,治疗师的有限和在家跟踪工作的困难限制了其有效性。一个易于使用的自动诊断系统可以通过向病人提供反馈和进行电传意识来缓解家庭治疗的困难。在这项工作中,我们建议建立一个新型结构(T-Net),用于自动检测和分类未剪辑的视频。T-Net结合时间检测和分解,并运行可以由临床医生解释的特征。我们将T-Net与一些在原始视频中提取的深层特征上工作的最先进的系统进行比较,T-Net在依赖临床实践所需的可解释性特征的同时,在平均精确性方面实现可比的绩效。