Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.

Journal: Sleep health
PMID: 39788836

Abstract

GOAL AND AIMS: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes.

Authors

  • Jyotirmoy Nirupam Das
    Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA. Electronic address: jfd5895@psu.edu.
  • Linying Ji
    Department of Psychology, Montana State University, Bozeman, Montana, USA.
  • Yuqi Shen
    Biobehavioral Health Department, The Pennsylvania State University, State College, Pennsylvania, USA.
  • Soundar Kumara
    Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
  • Orfeu M Buxton
    Department of Biobehavioral Health Department, The Pennsylvania State University, University Park, Pennsylvania, USA.
  • Sy-Miin Chow
    Department of Human and Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.

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