Collaborative Research: Computational Behavioral Science: Modeling, Analysis, and Visualization of Social and Communicative Behavior

合作研究：计算行为科学：社交和交流行为的建模、分析和可视化

• 批准号: 1029549
• 负责人: Anind Dey
• 金额: $ 149.95万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029549&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Behavioral; Science

Computational Behavioral Science: Modeling, Analysis, and Visualization of Social and Communicative BehaviorLead PI/Institution: James M. Rehg, Georgia Institute of TechnologyThis Expedition will develop novel computational methods for measuring and analyzing the behavior of children and adults during face-to-face social interactions. Social behavior plays a key role in the acquisition of social and communicative skills during childhood. Children with developmental disorders, such as autism, face great challenges in acquiring these skills, resulting in substantial lifetime risks. Current best practices for evaluating behavior and assessing risk are based on direct observation by highly-trained specialists, and cannot be easily scaled to the large number of individuals who need evaluation and treatment. For example, autism affects 1 in 110 children in the U.S., with a lifetime cost of care of $3.2 million per person. By developing methods to automatically collect fine-grained behavioral data, this project will enable large-scale objective screening and more effective delivery and assessment of therapy. Going beyond the treatment of disorders, this technology will make it possible to automatically measure behavior over long periods of time for large numbers of individuals in a wide range of settings. Many disciplines, such as education, advertising, and customer relations, could benefit from a quantitative, data-drive approach to behavioral analysis. Human behavior is inherently multi-modal, and individuals use eye gaze, hand gestures, facial expressions, body posture, and tone of voice along with speech to convey engagement and regulate social interactions. This project will develop multiple sensing technologies, including vision, speech, and wearable sensors, to obtain a comprehensive, integrated portrait of expressed behavior. Cameras and microphones provide an inexpensive, noninvasive means for measuring eye, face, and body movements along with speech and nonspeech utterances. Wearable sensors can measure physiological variables such as heart-rate and skin conductivity, which contain important cues about levels of internal stress and arousal that are linked to expressed behavior. This project is developing unique capabilities for synchronizing multiple sensor streams, correlating these streams to measure behavioral variables such as affect and attention, and modeling extended interactions between two or more individuals. In addition, novel behavior visualization methods are being developed to enable real-time decision support for interventions and the effective use of repositories of behavioral data. Methods are also under development for reflecting the capture and analysis process to users of the technology.The long-term goal of this project is the creation of a new scientific discipline of computational behavioral science, which draws equally from computer science and psychology in order to transform the study of human behavior. A comprehensive education plan supports this goal through the creation of an interdisciplinary summer school for young researchers and the development of new courses in computational behavior. Outreach activities include significant and on-going collaborations with major autism research centers in Atlanta, Boston, Pittsburgh, Urbana-Champaign, and Los Angeles.

计算行为科学：社会和交流行为领域的建模，分析和可视化PI/机构：乔治亚技术研究所James M. Rehg将开发出新的计算方法，用于衡量和分析面对面社会互动期间儿童和成人的行为。社会行为在童年期间在获得社会和沟通技巧方面起着关键作用。患有自闭症等发育障碍的儿童在获得这些技能方面面临巨大挑战，从而带来了巨大的终生风险。当前评估行为和评估风险的最佳实践是基于受过良好训练的专家的直接观察，并且不能轻易地缩放到需要评估和治疗的大量个人。例如，自闭症影响美国110名儿童中有1人，一生的护理费用为每人320万美元。通过开发自动收集细粒行为数据的方法，该项目将实现大规模的客观筛查以及更有效的交付和评估治疗。超越了疾病的治疗，这项技术将使在各种环境中的大量个人长时间自动衡量行为是可能的。许多学科，例如教育，广告和客户关系，可以从定量的数据驱动方法中受益于行为分析。人类的行为本质上是多模式的，个人使用眼睛目光，手势，面部表情，身体姿势和语气，以及语音，以传达参与并调节社会互动。 该项目将开发多种传感技术，包括视觉，言语和可穿戴传感器，以获取表达行为的全面，整合的肖像。摄像机和麦克风提供了一种廉价的无创手段，用于测量眼睛，面部和身体运动以及语音和非语音话语。可穿戴的传感器可以测量生理变量，例如心率和皮肤电导率，这些变量包含有关内部应力水平和唤醒水平与表达行为相关的重要提示。该项目正在开发独特的功能，以同步多个传感器流，将这些流相关联，以衡量行为变量，例如情感和注意力，并建模两个或多个个体之间的扩展相互作用。此外，正在开发新的行为可视化方法，以实现干预措施的实时决策支持和有效使用行为数据存储库。方法还正在开发用于反映技术用户的捕获和分析过程。该项目的长期目标是创建了一项新的计算行为科学科学学科，该学科同样地从计算机科学和心理学中汲取灵感，以改变人类行为的研究。一项全面的教育计划通过为年轻研究人员的跨学科暑期学校和计算行为新课程的开发来支持这一目标。外展活动包括与亚特兰大，波士顿，匹兹堡，乌尔巴纳·坎佩恩和洛杉矶的主要自闭症研究中心的重要合作和持续的合作。