Abstract
Recently, growing health awareness, novel methods allow individuals to monitor sleep at home. Utilizing sleep sounds offers advantages over conventional methods like smartwatches, being non-intrusive, and capable of detecting various physiological activities. This study aims to construct a machine learning-based sleep assessment model providing evidence-based assessments, such as poor sleep due to frequent movement during sleep onset. Extracting sleep sound events, deriving latent representations using VAE, clustering with GMM, and training LSTM for subjective sleep assessment achieved a high accuracy of 94.8% in distinguishing sleep satisfaction. Moreover, TimeSHAP revealed differences in impactful sound event types and timings for different individuals.
Abstract (translated)
近年来，随着健康意识的不断提高，人们可以在家监测睡眠。利用睡眠声音来实现传统方法如智能手表等是不侵入性的，并且能够检测到各种生理活动。本研究旨在构建一个基于机器学习的睡眠评估模型，提供基于证据的评估，例如睡眠开始时频繁运动导致的睡眠质量差。通过提取睡眠声音事件，使用VAE生成潜在表示，聚类使用GMM，并使用LSTM进行主观睡眠评估，该模型的区分睡眠满意度高达94.8%。此外，TimeSHAP揭示了不同个体对影响性声音事件和时间的差异。
URL
https://arxiv.org/abs/2404.10299
