Performance Evaluation of a Machine Learning-Based Methodology Using Dynamical Features to Detect Nonwear Intervals in Actigraphy Data in a Free-Living Setting

Details

• Personal Author:
  Buxton OM ; Chow S-M ; Das JN ; Ji L ; Kumara S ; Shen Y
• Description:
  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. Focus technology: Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics). Reference technology: The built-in nonwear sensor as "ground truth" to classify nonwear periods using other data, mimicking features of Actiwatch 2. Sample: Data were collected over 1 week from employed adults (n = 853). Design: Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows. Core analytics: The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features. Core outcomes: The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms. Important supplemental outcomes: The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified. Core conclusion: Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety Sleep
• Keywords:
  Actigraphy Activity Levels Algorithms Author Keywords: Nonwear Detection Machine Learning Sensors Wearable Technologies
• ISSN:
  2352-7218
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Denver Region [OSHA] ; Montana ; Oregon ; Pennsylvania ; Philadelphia Region [OSHA] ; San Francisco Region [OSHA]
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  166-173
• Volume:
  11
• Issue:
  2
• NIOSHTIC Number:
  nn:20070912
• Citation:
  Sleep Health 2025 Apr; 11(2):166-173
• Contact Point Address:
  Jyotirmoy Nirupam Das, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
• Email:
  jfd5895@psu.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  Portland State University
• Peer Reviewed:
  True
• Start Date:
  20050901
• Source Full Name:
  Sleep Health
• End Date:
  20081130
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:63faeddd8e526ba4bf8fbb79c533e5ca478fbd75411b7c5ac04258bf973d82ca93d8ebaca11b312ad87f4dab3269f6796bd7090766ae80029063e3612f35b1f7
• Download URL:
  https://stacks.cdc.gov/view/cdc/209034/cdc_209034_DS1.pdf
• File Type:
  [PDF - 2.68 MB ]