Abstract
Wearable actimeters can improve our understanding of sleep in the natural environments. Current algorithms may produce inaccuracies in specific individuals and circumstances, such as quiet wakefulness. New hardware allows data collection at higher frequencies enabling sophisticated analytical methods. We have developed a novel statistical algorithm, the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), to identify behavioral states from recordings of everyday movement. WACSAW employs optimal transport techniques to identify segments with differing activity variability. Functions characterizing the segments' movement distributions were clustered into two groups using a k-nearest neighbors and labeled as sleep or wake based on their proximity to an idealized sleep distribution. It returned >95% overall accuracy validated against participant logs in the test data and performed ~10% better than a clinically validated actimetry system. We present the methodology describing how WACSAW results in a novel, individually tuned, statistical approach to actimetry that improves sleep/wakefulness classification and provides auxiliary information as part of the calculations that can be further related to sleep-relevant outcomes.
Recommended Citation
A. Vandegriffe et al., "WACSAW: An Adaptive, Statistical Method to Classify Movement into Sleep and Wakefulness States," Plos One, vol. 20, no. 12 December, article no. e0333417, Public Library of Science, Dec 2025.
The definitive version is available at https://doi.org/10.1371/journal.pone.0333417
Department(s)
Mathematics and Statistics
Second Department
Biological Sciences
Publication Status
Open Access
International Standard Serial Number (ISSN)
1932-6203
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2025
PubMed ID
41379801
