Whereas it has become easier for individuals to track their personal health data (e.g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work towards striving closer to their health goals. We aim to bridge the gap between data collection and explanation generation by mining the data for interesting behavioral findings that may provide hints about a user's tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or "protoforms." These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual users, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user data and show that our system can generate interesting and useful explanations.

中文翻译：

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从时间性个人健康数据生成解释的框架

尽管个人跟踪个人健康数据（例如心率、步数、食物记录）变得更加容易，但在收集数据和生成有意义的解释以帮助用户更好地了解他们的健康数据之间仍然存在巨大鸿沟。数据对他们来说意味着。随着对数据理解的加深，用户将能够根据新发现的信息采取行动，努力实现更接近他们的健康目标。我们的目标是通过挖掘数据寻找有趣的行为发现来弥合数据收集和解释生成之间的差距，这些发现可能会提供有关用户倾向的提示。我们的重点是通过一组内容丰富的摘要模板或“原型”来提高临时个人健康数据的可解释性。这些原型涵盖了帮助用户评估其健康目标的基于评估的摘要和解释其隐性行为的基于模式的摘要。除了个人用户之外，我们使用的原型也是为人群级别的总结而设计的。我们应用我们的方法从真实用户数据生成摘要（单变量和多变量），并表明我们的系统可以生成有趣且有用的解释。