论文标题
使用消费者设备和服务的跟踪数据来预测和可视化人们的日常情绪
Predicting and Visualizing Daily Mood of People Using Tracking Data of Consumer Devices and Services
论文作者
论文摘要
用户可以轻松地从设备（例如气象站和健身追踪器）和服务（例如，在GitHub上使用屏幕时间跟踪器和提交）的个人数据导出个人数据，但他们很难获得宝贵的见解。为了解决这个问题，我们介绍了称为InsightMe的自追踪元应用程序，该应用程序旨在向用户展示数据与他们的福祉，健康和绩效的关系。本文着重于情绪，这与健康密切相关。随着一个人收集的数据，我们展示了一个人的睡眠，运动，营养，天气，空气质量，放映时间以及工作与人白天所经历的平均情绪相关。此外，该应用程序通过多个线性回归和神经网络预测情绪，分别达到了0.55和0.50的解释方差。我们通过显示相关性的p值（绘制预测间隔）来努力提高解释性和透明度。此外，我们进行了一项小型A-B测试，以说明原始数据如何影响预测。源代码和应用程序可在线提供。
Users can easily export personal data from devices (e.g., weather station and fitness tracker) and services (e.g., screentime tracker and commits on GitHub) they use but struggle to gain valuable insights. To tackle this problem, we present the self-tracking meta app called InsightMe, which aims to show users how data relate to their wellbeing, health, and performance. This paper focuses on mood, which is closely associated with wellbeing. With data collected by one person, we show how a person's sleep, exercise, nutrition, weather, air quality, screentime, and work correlate to the average mood the person experiences during the day. Furthermore, the app predicts the mood via multiple linear regression and a neural network, achieving an explained variance of 0.55 and 0.50, respectively. We strive for explainability and transparency by showing the users p-values of the correlations, drawing prediction intervals. In addition, we conducted a small A-B test on illustrating how the original data influence predictions. The source code and app are available online.
