@inproceedings{50caaff1fc8342b89f11404ccd56e750,
title = "LOL: Learning with One Lightweight Sensor-Aware Predictive Model For Sleep Quality",
abstract = "This study proposes an approach to predict human sleep quality using only a machine learning model, CatBoost. Based on sensor data collected from 10 participants, the model aims to predict six target indicators related to sleep quality. To this end, four representative preprocessing techniques - Hourly Aggregation, Linear Interpolation, Explicit Zero Imputation, and Derived Feature Engineering - were tailored to the characteristics of each of the 12 sensor datasets and applied accordingly. CatBoost was chosen for its low variance and strong generalization capability, which enable robust performance without relying on complex architectures, as required in many deep learning models. Experimental results demonstrated that the proposed model achieved a macro F1-score of 0.6685, highlighting its strong predictive performance with only a CatBoost-based approach.",
keywords = "Catboost, Data preprocessing, Lifelog data, Lightweight model, Machine Learning",
author = "Koo, \{Ja Hyeob\} and \{Ho Song\}, Yong and Shim, \{Jae Hyeon\} and \{Yeong Lee\}, So and Lee, \{Young Hoon\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 16th International Conference on Information and Communication Technology Convergence, ICTC 2025 ; Conference date: 14-10-2025 Through 17-10-2025",
year = "2025",
doi = "10.1109/ICTC66702.2025.11389075",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1697--1702",
booktitle = "2025 16th International Conference on Information and Communication Technology Convergence, ICTC 2025",
}