GLANCE: GLAnceable Nuances for Contextual Events

这个项目将开发和验证令人兴奋的新方式，人们可以通过认知可穿戴设备与世界互动——智能的身体计算系统，旨在了解用户，环境，重要的是，是即时和有用的。具体来说，我们将专注于自动制作和显示我们所谓的可浏览引导。避开传统和复杂的3D增强现实方法，这些方法很难显示出显着的实用性，可浏览的指导旨在将复杂任务的细微差别综合在短片段中，这是可穿戴计算系统的理想选择，对用户的干扰较小，并且更容易学习和使用。有两个关键的研究挑战，第一个是能够从长，原始和无脚本的可穿戴视频中挖掘信息，这些视频是从真实的用户-对象交互中获取的，以便生成可浏览的支持。另一个关键挑战是如何自动检测用户的不确定时刻，在此期间应该在没有用户明确提示的情况下提供支持。该项目旨在解决以下基本问题：1。通过从可穿戴视觉和惯性传感器的连续流中健壮地确定与任务相关的对象交互对应的时间段，改进对用户注意力的检测。2 .仅在需要时提供帮助，从多个用户自主识别的微交互中建立用户、上下文和任务的模型，重点关注能够促进指导的模型。识别和预测来自可穿戴传感器的行动不确定性，特别是凝视模式和头部运动。检测和权衡用户的专业知识，以识别任务的细微差别，从而实现实时定制指导的最佳创建。基于自主构建的模型，设计并交付可浏览的指导，在任务执行过程中以无缝和无提示的方式发挥作用，干扰最小。GLANCE的基础是丰富的实验工作项目，以及对各种交互任务和用户组的严格验证。需要测试的人群包括熟练人群和普通人群，测试的任务包括：装配、使用新设备（如未知的咖啡机）和修理任务（如更换自行车齿轮电缆）。它还紧密结合了工作演示的开发。与我们的合作伙伴合作，该项目将探索与辅助生活和工业环境中的医疗保健相关的高价值影响案例，重点是装配和维护任务。我们的团队是计算机科学与行为科学之间的协作，开发一种新颖的数据挖掘和计算机视觉算法，了解用户何时以及如何需要支持。

This project will develop and validate exciting novel ways in which people can interact with the world via cognitive wearables -intelligent on-body computing systems that aim to understand the user, the context, and importantly, are prompt-less and useful. Specifically, we will focus on the automatic production and display of what we call glanceable guidance. Eschewing traditional and intricate 3D Augmented Reality approaches that have been difficult to show significant usefulness, glanceable guidance aims to synthesize the nuances of complex tasks in short snippets that are ideal for wearable computing systems and that interfere less with the user and that are easier to learn and use.There are two key research challenges, the first is to be able to mine information from long, raw and unscripted wearable video taken from real user-object interactions in order to generate the glanceable supports. Another key challenge is how to automatically detect user's moments of uncertainty during which support should be provided without the user's explicit prompt.The project aims to address the following fundamental problems:1. Improve the detection of user's attention by robustly determining periods of time that correspond to task-relevant object interactions from a continuous stream of wearable visual and inertial sensors.2. Provide assistance only when it is needed by building models of the user, context and task from autonomously identified micro-interactions by multiple users, focusing on models that can facilitate guidance.3. Identify and predict action uncertainty from wearable sensing in particular gaze patterns and head motions.4. Detect and weigh user expertise for the identification of task nuances towards the optimal creation of real-time tailored guidance.5. Design and deliver glanceable guidance that acts in a seamless and prompt-less manner during task performance with minimal interruptions, based on autonomously built models.GLANCE is underpinned by a rich program of experimental work and rigorous validation across a variety of interaction tasks and user groups. Populations to be tested include skilled and general population and for tasks that include: assembly, using novel equipment (e.g. an unknown coffee maker), and repair tasks (e.g. replacing a bicycle gear cable). It also tightly incorporates the development of working demonstrations.And in collaboration with our partners the project will explore high-value impact cases related to health care towards assisted living and in industrial settings focusing on assembly and maintenance tasks. Our team is a collaboration between Computer Science, to develop a the novel data mining and computer vision algorithms, and Behavioral Science to understand when and how users need support.