PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level
This study addresses the problem of sleep quality and stress level estimation for individuals, particularly those using lifelog datasets, and presents an incremental solution.
The study tackled the problem of accurately assessing sleep quality and stress levels through lifelog analysis, resulting in more significant results than various data formats. The proposed PixleepFlow framework produced promising outcomes, although specific numbers are not provided.
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.