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

该奖项支持的研究正在开发基于社区的方法，用于从身体佩戴的传感器感知，识别和解释人类活动。具体来说，这项研究是1）开发系统，学习新的活动类别与最小的人类监督，其中系统询问人类用户的额外信息的活动正在学习，但只有当这样的查询是必要的信息和行为不显眼，2）开发社区指导学习的范式，它利用人们的社会关系和行为相似性，以确定一个有效的方案，用于在许多人之间共享基础活动课程的各个方面; 3）通过使用新的社区指导学习方法来评估（a）老年人的社会隔离和功能独立性，以及（B）高功能自闭症儿童的社会互动。一般来说，该研究正在推进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，由此产生的研究有可能通过长期连续、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，部分原因是它正在将机器学习和人工智能推向社会和社会重要领域，有望吸引计算机科学领域代表性不足的群体，特别是女性。

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.