Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment
  and Sleep Individuality Analysis
Shintaro Tamai Masayuki Numao Ken-ichi Fukui
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.