SCH: INT: Collaborative Research: Learning and Sensory-based Modeling for Adaptive Web-Empowerment Trauma Treatment

灾难、兵役、事故、家庭暴力和其他创伤性事件造成的精神创伤是一个健康问题，每年造成数十亿美元的损失。除了直接成本，还有间接成本，包括医疗和精神病护理的使用增加了45-150%。虽然已经开发了基于网络的支持系统，但这些系统实际上是一种“一刀切”的方法，缺乏定期治疗的个性化以及与定制方案相关的参与和有效性。该项目汇集了创伤治疗，面部分析，计算机视觉和机器学习领域的领先研究人员组成的多学科团队，以开发一种可扩展的，自适应的以人为本的方法，该方法使用视觉和传感来改善基于网络的创伤治疗。 特别是，这项工作在治疗过程中测量特定的个性化变量，然后使用一个模型来适应有需要的个人的治疗。核心治疗设计建立在成熟的社会认知理论基础上，其中自我效能和生理反应是恢复的关键因素。该项目衡量这些以及在自我指导的基于网络的治疗中至关重要的参与度。建模需要计算机视觉和面部分析的进步，以开发只需标准笔记本电脑即可计算的个性化模型。这个项目是第一次尝试从感官数据中近似自我效能的变化。这项工作使用并推进了机器学习和领域自适应来支持这种近似，并支持模型的快速个性化。 建立一个智能系统，通过结合感知和学习来增强个人的能力，以改善基于网络的治疗，为满足国民健康需求提供了一种具有成本效益的循证创伤治疗的变革性方法。

Mental trauma following disasters, military service, accidents, domestic violence and other traumatic events is a health issue costing multiple billion of dollars per year. Beyond its direct costs, there are indirect costs including a 45-150% greater use of medical and psychiatric care. While web-based support systems have been developed these are effectively a "one-size-fits all" approach lacking the personalization of regular treatment and the engagement and effectiveness associated with a tailored regimen. This project brings together a multi-disciplinary team of leading researchers in trauma treatment, facial analysis, computer vision and machine learning to develop a scalable, adaptive person-centered approach that uses vision and sensing to improve web-based trauma treatment. In particular, the effort measures specific personalized variables during treatment and then uses a model to adapt treatment to individuals in need. The core treatment design builds on well-established social-cognitive theory, where self-efficacy and physiological response are critical elements of recovery. The project measures these as well as engagement that is critical in self-directed web-based treatment. The modeling requires advances in computer vision and facial analysis to develop individualized models that can be computed with just a standard laptop. This project is the first effort to approximate changes in self-efficacy from sensory data. The effort uses and advances machine learning and domain adaption to support this approximation as well as to support the rapid personalization of models. Building a smart system that empowers individuals by combining sensing and learning to improve web-based treatment offers a transformative approach to this national health need for cost-effective evidence-based treatment of trauma.