ChatPPG.
Paper

Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data

Huang Q, Cohen D, Komarzynski S, Li XM, Innominato P, Lévi F +1 more
J R Soc Interface 20182018
Read paper Cite
Published articlePublication date: Feb 2018Added to atlas: Oct 15, 2026Source
Citations62Crossref referencesChecked Oct 16, 2026
GitHub stars—Not available
HF downloads—Not available

Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Their interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising the authors propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates, based on probabilities of transitions between rest and activity, that are interpretable and of interest to circadian research.

  • Venue: J R Soc Interface 2018
At a glance
Venue
J R Soc Interface 2018
Published
2018
DOI
10.1098/rsif.2017.0885 (opens in a new tab)
arXiv
Not reported
PMID
Not reported
Licence
Not reported
Signals
Not reported
Form factor
Not reported
Subjects
Not reported
Code
Not reported
Added
Oct 15, 2026

Abstract

Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates, based on probabilities of transitions between rest and activity, that are interpretable and of interest to circadian research.

Verbatim fromthe publisher's DOI record· Publisher licence: https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data: ChatPPG research summary card

Cite

@article{huanghmmcircadianrhythmi2018,
  title = {{Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data}},
  author = {Huang Q and Cohen D and Komarzynski S and Li XM and Innominato P and Lévi F and Finkenstädt B},
  year = {2018},
  journal = {J R Soc Interface 2018},
  doi = {10.1098/rsif.2017.0885}
}

History

  1. Added to the ChatPPG research hub

More on ChatPPG