CAREER: Enabling Community-Scale Modeling of Human Behavior and its Application to Healthcare

职业：实现社区规模的人类行为建模及其在医疗保健中的应用

• 批准号: 0845683
• 负责人: Tanzeem Choudhury
• 金额: $ 50.75万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2011-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845683&HistoricalAwards=false
• 关键词: CAREER; Enabling; Community; Scale; Modeling

Research supported by this award is developing community-based methods for sensing, recognizing, and interpreting human activities from body-worn sensors. Specifically, this research is1) developing systems that learn new classes of activity with minimal human supervision, where the system queries a human user for additional information on an activity being learned, but only when such queries are informationally necessary and behaviorally unobtrusive,2) developing the paradigm of community-guided learning, which leverages people's social ties and behavioral similarities, in order to define an efficient scheme for sharing various aspects of the underlying activity classes across many individuals, and3) evaluating the new community-guided learning methods by using them to learn about (a) social isolation and functional independence among elderly persons, and (b) social interaction among high-functioning autistic children.Speaking generally, the research is advancing machine learning and artificial intelligence, especially in the areas of semi-supervised, active, and relational learning. Beyond these basic scientific contributions, the resulting research has the potential to transform community health assessment by collecting fine-grained clinically-relevant information continuously, cheaply, and unobtrusively, over long periods of time. This research also opens up many opportunities for education and outreach, in part because it is pushing machine learning and artificial intelligence into social and societally-important realms, promising to attract groups, notably women, who are under-represented in computer science.

该奖项支持的研究正在开发基于社区的方法，从穿戴在身上的传感器感知、识别和解释人类活动。具体地说，这项研究是：1)开发在最少人工监督下学习新的活动类别的系统，其中该系统向人类用户询问关于正在学习的活动的附加信息，但仅当这样的询问在信息上是必要的并且在行为上不引人注目时；2)开发社区引导学习的范例，其利用人们的社会关系和行为相似性，以便定义在许多个人之间共享基础活动类别的各个方面的有效方案；以及3)通过使用新的社区引导学习方法来评估新的社区引导学习方法，以了解(A)老年人之间的社会隔离和功能独立，和(B)高功能自闭症儿童之间的社会互动。总的来说，这项研究正在推动机器学习和人工智能，特别是在半监督、主动和关系学习领域。除了这些基本的科学贡献外，由此产生的研究还有可能通过在较长时间内连续、廉价和不引人注目地收集细粒度的临床相关信息来改变社区健康评估。这项研究还为教育和推广提供了许多机会，部分原因是它正在推动机器学习和人工智能进入社会和社会重要领域，有望吸引群体，尤其是在计算机科学中代表性不足的女性。