Streamlined Tools for Personal Analytics

这项发现补助金将专注于设计新的工具和应用程序，使“以思维的速度记录”-允许人们在不消耗大量时间或注意力的情况下捕捉关于他们的想法和活动的详细，频繁的观察。* 最近的个人传感器和自我跟踪工具的浪潮已经承诺帮助人们使用数据来更好地了解自己。通过跟踪我们的生理和活动，自我跟踪工具应该允许日常用户测量，理解和处理慢性疼痛和情绪健康等问题，或支持学习新语言等持续目标。然而，像步数和应用程序日志这样容易自动化的测量通常太低级，无法回答这类问题。同时，当前用于手动日志记录的移动的应用程序通常非常麻烦、耗时，并且与日常活动不兼容，使得人们无法保持一致或准确的记录。然而，许多重要的个人数据-包括疼痛日志，情绪日志和语言使用笔记-需要人工判断和观察，必须手动记录。我的研究将探索简化个人数据收集和分析的工具和技术。通过最大限度地减少与自日志相关的开销，我们的原型将使收集和分析更大量的丰富个人数据成为可能。这些工具将允许人们在日常活动和想法的背景下快速记录信息（我在哪里经历疼痛，疼痛有多强烈？我的环境有什么变化吗？我是不是把这个词念错了？）在其他情况下很难做到的时候。轻量级的记录工具将帮助人们记录活动，而不会失去对当前任务的关注，这使得记录和分析更多类型的活动成为可能，并且还支持疼痛跟踪等应用程序，其中记录理想情况下不应该将人的注意力集中在症状上。 * 我的团队的研究将通过设计简化数据收集和分析的工具包来研究这些挑战。我们将设计基于语音和触摸的最低限度的日志记录方法，尽可能平滑和可靠，并探索微妙的反馈机制，帮助人们检测错误并添加结构以支持后续分析。这些日志工具将由量身定制的处理和分析工具支持，使人们能够在分析之前轻松过滤，清理和组织日志，并构建可重用的分析管道，用于检查未来的数据。最后，我们将探索这些轻量级日志工具在一系列特定领域应用程序中的潜力，包括疼痛管理和语言学习工具。**

This Discovery Grant will focus on the design of new tools and applications that enable "logging at the speed of thought"—allowing people to capture detailed, frequent observations about their thoughts and activities without consuming significant time or attention. ***The recent wave of personal sensors and self-tracking tools has promised to help people use data to better understand themselves. By making it possible to track our physiology and our activities, self-tracking tools should allow everyday users to measure, understand, and deal with issues like chronic pain and emotional health, or support ongoing goals like learning a new language. However, easily automated measurements like step counts and application logs are often too low-level to answer these kinds of questions. Meanwhile, current mobile applications for manual logging are often so cumbersome, time consuming, and incompatible with daily activities that people fail to keep consistent or accurate records. However, many important pieces of personal data—including pain logs, emotion journals, and notes on language use—require human judgement and observation and must be recorded manually.***My research will explore tools and techniques for streamlining personal data collection and analysis. By minimizing the overhead associated with self-logging, our prototypes will make it possible to collect and analyze much greater quantities of rich personal data. These tools will allow people to quickly record information in the context of their daily activities and thoughts (Where am I experiencing pain and how intense is it? Did something change in my environment? Did I just mispronounce that word?) at times when it would otherwise be difficult to do so. Lightweight recording tools will help people record activities without losing focus on their current task—making it possible to record and analyse more types of activities, and also supporting applications like pain tracking where logging ideally shouldn't focus the person's attention on symptoms as they are occurring. ***My team's research will examine these challenges through the design of a toolkit for streamlined data collection and analysis. We will design minimalist logging approaches based on speech and touch that are as smooth and reliable as possible, and explore subtle feedback mechanisms that help people detect errors and add structure to support later analysis. These logging tools will be backed by tailored processing and analysis tools that allow people to easily filter, clean, and organize their logs before analysis and build reusable analysis pipelines that they can use to examine future data. Finally, we will explore the potential of these lightweight logging tools in a series of domain-specific applications, including tools for pain management and language learning. **