CHS: Medium: Collaborative Research: Activity Recognition for Reducing Delays in Fast-Response Teamwork

在时间紧迫的团队合作环境中，人的表现依赖于适当和及时地完成任务。时间感知是一种影响团队绩效的关键认知功能，在这些环境中经常受到扭曲，并受到认知工作量的影响。该项目将通过分析口头交流和信息工件的使用，自动地、不显眼地建模和跟踪跨学科任务的执行，从而解决时效性错误。生成的模型将用于显示关于关键任务时效性的警报，以支持团队成员的信息需求，而不会增加他们的认知工作量。该方法的收益和成本将在模拟设置中进行评估，测量其对团队绩效和总体目标完成的影响，以及对工作量和分心的影响。本研究的应用领域为创伤复苏、急诊损伤患者的早期评估与管理；目标是提高时间意识，以提高创伤团队的效率和患者的治疗效果，节省资金和生命。此外，该项目将为计算机科学和医学的学生提供跨学科教育的机会。该项目将开发用于监控团队工作进度和显示关键任务及时性警报的技术。关键的系统组件将包括识别活动、建模过程偏差和延迟，以及以一种不会转移工作注意力的方式显示过程信息。在快节奏和拥挤的协作环境中，活动识别的新技术将基于无源RFID、语音识别和计算机视觉，辅以其他传感器和数字设备。本研究将建立(1)快速团队合作中复杂活动中的语言交流和数字文档交互的时间模型，以实现自动活动识别；(2)在多达一千个射频识别标签的情况下实时识别一百多种不同活动的方法；(3)在高任务负荷下显示延迟信息以便快速同化的方法，即从工人的反应中学习以提高其对团队的有用性。总之，这项工作将为不仅在创伤领域，而且在其他具有复杂、交叉任务的领域（如外科手术、交通控制和灾难管理）的团队提供计算机支持的构建块。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Human performance in time-critical teamwork settings relies on appropriate and timely task completion. Time perception, a critical cognitive function that influences team performance, is often skewed in these settings and is impacted by cognitive workload. This project will address timeliness errors by automatically and unobtrusively modeling and tracking cross-disciplinary task performance through analysis of verbal communication and the use of information artifacts. The resulting model will be used to display alerts about timeliness of critical tasks in a way that supports team members' information needs without increasing their cognitive workload. The benefits and costs of this approach will be evaluated in a simulation setting, measuring its impact on team performance and overall goal accomplishment, as well as its impact on workload and distraction. The application domain for this research is trauma resuscitation, the early evaluation and management of injured patients in the emergency department; the goal is for increased temporal awareness to improve both trauma team efficiency and patient outcomes, saving money and lives. Further, the project will provide opportunities for interdisciplinary education involving students from computer science and medicine. This project will develop techniques for monitoring the progress of teamwork and displaying alerts about timeliness of critical tasks. The key system components will include recognizing activities, modeling process deviations and delays, and displaying process information in a way that does not divert attention from the work. Novel techniques for activity recognition in fast-paced and crowded collaborative settings will be based on passive RFID, speech recognition, and computer vision, supplemented by other sensors and digital devices. The proposed research will develop (1) temporal models of verbal communication and digital document interaction during complex activities in fast-paced teamwork for the purpose of automatic activity recognition; (2) approaches for real-time recognition of over a hundred different activities in the presence of up to a thousand RFID tags; and (3) approaches for displaying delay information for rapid assimilation under high task load that learn from workers' responses to improve their usefulness to the team. Together, the work will provide building blocks for computerized support of teams not just in the trauma domain, but in other domains with complex, interleaved tasks such as surgery, traffic control, and disaster management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.