Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis

arXiv:2404.10299v1 Announce Type: new
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 …

abstract accuracy advantages analysis arxiv assessment augmentation clustering construct cs.ai cs.lg cs.sd data eess.as health home machine machine learning novel sleep smartwatches study type