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

SCH：INT：协作研究：自适应网络赋权创伤治疗的学习和基于感觉的建模

• 批准号: 1418520
• 负责人: Terrance Boult
• 金额: $ 149.36万
• 依托单位: University of Colorado at Colorado Springs
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418520&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Learning

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.

灾难、服兵役、事故、家庭暴力和其他创伤性事件造成的精神创伤是一个每年花费数十亿美元的健康问题。除了直接成本，还有间接成本，包括多使用45%-150%的医疗和精神护理。虽然已经开发了基于网络的支助系统，但这些系统实际上是一种“一刀切”的办法，缺乏常规治疗的个人化以及与量身定制的方案相关的参与性和有效性。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、以人为中心的适应性方法，该方法使用视觉和传感来改进基于网络的创伤治疗。特别是，这项工作在治疗期间测量特定的个性化变量，然后使用一个模型来使治疗适应有需要的个人。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康复的关键要素。该项目衡量了这些因素，以及在自我导向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，以开发仅用标准笔记本电脑就能计算的个性化模型。这个项目是第一次从感官数据中近似自我效能感的变化。该工作使用并推进了机器学习和领域自适应，以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改进基于网络的治疗，从而增强个人的能力，为满足国家对创伤的成本效益和循证治疗的需求提供了一种变革性的方法。