EAR: Eye-based Activity Recognition

活动识别涉及通过观察用户的行为来推断人类活动，以便于系统在日常活动中主动支持用户。应用领域包括健康、安全、认知辅助、生活方式监测、娱乐和能源管理。在这个项目中，我们建议通过研究眼动模式作为一种新的上下文线索来促进活动识别。使眼球运动成为与活动相关的信息的一个非常独特的来源是它们与视觉认知的直接联系。这意味着眼球运动数据有可能不仅提供活动发生的在线指示，而且还提供潜在的认知过程的指示，例如注意力。眼球运动，包括其测量和分析，在临床眼科学和认知科学中已经得到了广泛的研究，但将其用于日常环境中的在线活动识别是一个新的命题。主要的挑战包括适合日常环境的测量技术的适应，在现实条件下可以挖掘的信息的质量，以及基于眼睛的上下文在现实世界应用中的实际效用的演示。该项目被设计为基于眼睛的活动识别的可行性和潜力的初步调查。这项研究将包括对眼睛跟踪技术的评估，特别是对跟踪眼睛特征的光学方法的评估，以及用放置在眼睛附近的皮肤电极测量眼睛运动的眼电图法(EOG)。在评估的基础上，我们将对日常生活中的眼睛模式进行探索性研究。这里的目的是深入了解眼球运动揭示的日常活动，并开发从眼球运动模式中提取特征的方法，这些特征可能对活动和上下文识别有用。该工作的第三部分将研究基于眼睛的上下文信息在交互系统中的应用。该计划将开发两个实际演示，第一个探索纵向眼动分析如何有助于健康和生活方式监测，第二个展示眼睛运动如何用于任务辅助(例如，当眼睛指示视觉搜索活动时，搜索辅助)。

Activity recognition is concerned with inference of human activity from observation of the user's actions, to facilitate systems that proactively support users in everyday activity. Application areas include health, safety, cognitive assistance, lifestyle monitoring, entertainment, and energy management. In this project, we propose to advance activity recognition by investigating patterns of eye movement as a novel contextual cue. What makes eye movement a very distinct source of activity-related information is their direct link to visual cognition. This means that eye movement data has the potential to provide an online indication not only of what activity occurs but also of underlying cognitive processes, for example attention. Eye movements, including their measurement and analysis, have been studied extensively in clinical ophthalmology and cognitive science, but their use for on-line activity recognition in everyday settings is a novel proposition. Significant challenges include the adaptation of measurement techniques fit for everyday settings, the quality of information that can be mined under realistic conditions, and the demonstration of practical utility of eye-based context in real-world application.This project is designed as an initial investigation of the feasibility and potential of eye-based activity recognition. The research will include an assessment of eye tracking techniques, specifically of optical methods that track features on the eye, and of electrooculography (EOG) measuring eye movement with skin electrodes placed near the eyes. Based on the assessment, we will conduct explorative studies of eye patterns in daily life. The aim here is to gain insight into what eye movement reveals about everyday activity, and to develop methods for extraction of features from eye movement patterns that may be useful for activity and context recognition. A third part of the work will investigate application of eye-based context information in interactive systems. The plan is to develop two practical demonstrators, a first one exploring how longitudinal eye movement analysis may contribute to health and lifestyle monitoring, and a second one showing how eye movements might be used for task assistance (for example, search assistance when the eyes indicate visual search activity).