PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level

TL;DR

PixleepFlow is a novel image-based framework that converts lifelog data into composite images to accurately predict sleep quality and stress levels, leveraging sensor data and explainable AI for improved health insights.

Contribution

The paper introduces PixleepFlow, a new method that transforms lifelog data into images for better sleep and stress prediction, including an analysis of sensor importance using XAI.

Findings

PixleepFlow outperforms other data formats in prediction accuracy.

Sensor data significantly influences sleep and stress level estimations.

Explainable AI identifies key sensors impacting health assessments.

Abstract

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.