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

arXiv: https://arxiv.org/abs/2502.17469 · HTML: https://mlsanity.com/paper/2502.17469/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Full-Gradient Representation for Neural Network Visualization](https://mlsanity.com/paper/1905.00780/) (2019)
- [Human Understanding AI Paper Challenge 2024 -- Dataset Design](https://mlsanity.com/paper/2403.16509/) (2024)

## Built on by (most cited first, 3 of 3)

- [Understanding Human Daily Experience Through Continuous Sensing: ETRI Lifelog Dataset 2024](https://mlsanity.com/paper/2508.03698/) (2025)
- [MIS-LSTM: Multichannel Image-Sequence LSTM for Sleep Quality and Stress Prediction](https://mlsanity.com/paper/2509.11232/) (2025)
- [Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data](https://mlsanity.com/paper/2510.02835/) (2025)

## Abstract (arXiv)

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.