NREM3 Sleep Stage Estimation Based on Accelerometer by Body Movement Count and Biological Rhythms


  • Daiki Shintani The University of Electro-Communications
  • Iko Nakari The University of Electro-Communications
  • Satomi Washizaki Brain Sleep Co., LTD
  • Keiki Takadama The University of Electro-Communications



Impact of GenAI on Social and Individual Well-being


This paper proposes the method by physiological knowledge to improve the estimation performance of the NREM3 sleep based on the waist-attached accelerometer. Specifically, this paper proposes the hybrid method that combines the method based on body movement counts and the method based on biological rhythms of sleep. Through the human subject experiment, the following implications were revealed: (1) the proposed method can outperform famous machine learning models (Random Forest and LSTM) trained with automatically generated features that do not sufficiently incorporate domain knowledge; (2) when the input features are based on domain knowledge, the estimator explicitly designed by humans can outperform the machine learning method; and (3) combining the body movement counting method and the biological rhythm-based method can suppress the error of the body movement counting method and reduce false positives.






Impact of GenAI on Social and Individual Well-being