SalienTrack：通过模型解释为半自动自我跟踪反馈提供显著信息
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
摘要：
自我跟踪可以提高人们对自己不健康行为的认识，并支持反思以告知行为改变。越来越多的新技术使跟踪变得更加容易，从而产生大量的跟踪数据。然而，这些信息中的大部分对于反思和自我意识来说并不突出。为了解决这种反思的负担，我们创建了SalienTrack框架，其目的是1）识别突出的跟踪事件，2）选择这些事件的突出细节，3）解释为什么它们是信息性的，以及4）将细节呈现为手动引发或自动显示的反馈。我们在营养跟踪的背景下实施了SalienTrack。为此，我们首先进行了一项实地研究，以收集1-5周内基于照片的移动食物跟踪。然后，我们报告我们如何使用这些数据来训练一个可解释的AI模型的显着性。最后，我们创建了界面来呈现突出的信息，并进行了形成性用户研究，以了解如何将SalienTrack集成到界面中进行反思。我们的主要贡献是SalienTrack框架，在一个重要而具有挑战性的自我跟踪环境中展示了其半自动反馈的实现，并讨论了该框架的更广泛用途。
英文摘要：
Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, leading to large amounts of tracked data. However, much of that information is not salient for reflection and self-awareness. To tackle this burden for reflection, we created the SalienTrack framework, which aims to 1) identify salient tracking events, 2) select the salient details of those events, 3) explain why they are informative, and 4) present the details as manually elicited or automatically shown feedback. We implemented SalienTrack in the context of nutrition tracking. To do this, we first conducted a field study to collect photo-based mobile food tracking over 1-5 weeks. We then report how we used this data to train an explainable-AI model of salience. Finally, we created interfaces to present salient information and conducted a formative user study to gain insights about how SalienTrack could be integrated into an interface for reflection. Our key contributions are the SalienTrack framework, a demonstration of its implementation for semi-automated feedback in an important and challenging self-tracking context and a discussion of the broader uses of the framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
