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Joint Bayesian Hidden Markov Model With Subject-Specific Transitions for Wearable Sensor Data.

Wenbo Fei1, Zhen Miao2, Tianchen Xu3

  • 1Department of Biostatistics, Columbia University, New York, New York, USA.

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|December 6, 2025
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Summary
This summary is machine-generated.

This study introduces a new Bayesian method for analyzing wearable sensor data to monitor Parkinson's disease (PD) symptoms. The approach improves accuracy and generalizability by analyzing multiple individuals simultaneously, offering better disease tracking.

Keywords:
Parkinson's diseaseaccelerometer datahierarchical Dirichlet processesnonparametric Bayesiansubject‐specific effects

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Area of Science:

  • Digital Health
  • Biomedical Data Science
  • Wearable Technology

Background:

  • Wearable devices offer objective, real-time digital biomarkers for healthcare.
  • Accelerometer data shows promise for monitoring movement disorders like Parkinson's disease (PD).
  • Current methods analyzing individual data are inefficient and lack generalizability.

Purpose of the Study:

  • To develop a joint nonparametric Bayesian method for analyzing multi-subject wearable sensor data.
  • To improve the accuracy and generalizability of hidden state estimation in Parkinson's disease monitoring.
  • To account for between-subject variability and enable simultaneous estimation across subjects.

Main Methods:

  • Extension of the hierarchical Dirichlet process autoregressive hidden Markov model (HDP-AR-HMM).
  • Incorporation of subject-specific transition parameters for simultaneous estimation.
  • Validation using simulated data and application to the BEAT-PD DREAM Challenge CIS-PD study.

Main Results:

  • The proposed method achieved higher accuracy in detecting true hidden states compared to alternative methods.
  • Demonstrated consistent hidden state estimation without pre-specifying the number of states.
  • Successfully applied to real-world free-living data for Parkinson's disease symptom monitoring.

Conclusions:

  • The joint nonparametric Bayesian method enhances the analysis of wearable sensor data for disease monitoring.
  • This approach offers a more efficient and generalizable solution for tracking Parkinson's disease progression.
  • The method holds significant potential for improving healthcare through objective, real-time digital biomarkers.