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

灾难、服兵役、事故、家庭暴力和其他创伤事件之后的精神创伤是一个每年花费数十亿美元的健康问题。除了直接费用外，还有间接费用，包括医疗和精神科护理的使用增加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，缺乏常规治疗的个性化，也缺乏量身定制方案的参与和有效性。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，开发了一种可扩展的、自适应的以人为中心的方法，利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了特定的个性化变量，然后使用一个模型来适应有需要的个体的治疗。核心治疗设计建立在完善的社会认知理论之上，其中自我效能和生理反应是康复的关键要素。该项目对这些因素以及参与程度进行了测量，这在基于网络的自我指导治疗中至关重要。建模需要先进的计算机视觉和面部分析技术来开发个性化的模型，这些模型只需一台标准的笔记本电脑就可以计算出来。这个项目是第一次尝试从感官数据来近似自我效能感的变化。这项工作使用并推进了机器学习和领域适应来支持这种近似，以及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，以改进基于网络的治疗，为具有成本效益的循证创伤治疗这一国家卫生需求提供了一种变革性的方法。

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.