用图像化方法分析生活数据，预测睡眠质量与压力水平。
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level
- 将多源传感器数据转为复合图像，分析睡眠与健康关系。
- 实验验证该方法在多种数据格式中表现最优。
- 结合可解释AI识别关键影响因素，适合健康监测研究者。
生活日志数据分析可为个体日常健康与福祉提供重要洞察。准确评估生活质量需依赖多元传感器及精确同步。为此，本研究提出基于图像的睡眠质量与压力水平估计流程（PixleepFlow）。PixleepFlow通过将数据转化为复合图像，分析睡眠模式对整体健康的影响。实验使用生活日志数据集，确定了最佳数据格式组合，并利用可解释人工智能（XAI）识别对生活质量影响最大的传感器信息。结果表明，PixleepFlow在各类数据格式中表现更优。本研究为一项书面竞赛项目，更多基于生活日志数据的发现详见第四节。PixleepFlow更多信息请访问 https://github.com/seongjiko/Pixleep。
原文摘要 · Abstract (English)
The analysis of lifelogs can yield valuable insights into an individual's daily life, particularly with regard to their health and well-being. The accurate assessment of quality of life is necessitated by the use of diverse sensors and precise synchronization. To rectify this issue, this study proposes the image-based sleep quality and stress level estimation flow (PixleepFlow). PixleepFlow employs a conversion methodology into composite image data to examine sleep patterns and their impact on overall health. Experiments were conducted using lifelog datasets to ascertain the optimal combination of data formats. In addition, we identified which sensor information has the greatest influence on the quality of life through Explainable Artificial Intelligence(XAI). As a result, PixleepFlow produced more significant results than various data formats. This study was part of a written-based competition, and the additional findings from the lifelog dataset are detailed in Section Section IV. More information about PixleepFlow can be found at https://github.com/seongjiko/Pixleep.
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