PixleepFlow：一种基于像素的生活日志框架，用于预测睡眠质量和压力水平
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level
AI总结：
本研究提出PixleepFlow，一种基于像素的生活日志框架，通过将传感器数据转换为复合图像，利用XAI分析，有效预测睡眠质量和压力水平，优于多种数据格式。
摘要：
生活日志的分析可以产生对个人日常生活的宝贵见解，特别是关于其健康和福祉方面。由于使用了多种传感器和精确同步，对生活质量的准确评估成为必要。为解决这一问题，本研究提出了基于图像的睡眠质量和压力水平估计流程（PixleepFlow）。PixleepFlow采用一种将数据转换为复合图像数据的方法来检查睡眠模式及其对整体健康的影响。实验使用生活日志数据集进行，以确定数据格式的最佳组合。此外，我们通过可解释人工智能（XAI）识别了哪些传感器信息对生活质量影响最大。结果表明，PixleepFlow比各种数据格式产生了更显著的结果。本研究是书面竞赛的一部分，来自生活日志数据集的额外发现详见第四节。有关PixleepFlow的更多信息，请访问https://github.com/seongjiko/Pixleep。
英文摘要：
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.
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。
