ArticleComputational and structural biotechnology journal2026
Hierarchical heated markov modeling for synthesizing activity data from wearable devices.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Wearable devices enable continuous acquisition of physiological and behavioral signals with broad utility in precision and population health. However, real-world datasets often have privacy constraints, irregular sampling, and activity-dependent recording, complicating data sharing and modeling. We present a synthesis framework that preserves statistical realism by matching marginal distributions and inter-record distances. Method: We propose a hierarchical heated Markov model (HHMM) that captures conditional dependencies and time-varying behavioral patterns. Tier 1 generates minute-level activity types using hour-specific priors and a heating mechanism to achieve a target ergodic distribution. Tier 2 samples heart rate (via Metropolis-Hastings conditioned on activity) and activity duration from empirical conditional distributions. Tier 3 synthesizes additional variables (e.g., sleep duration, calories) conditioned on Tiers 1-2, with deterministic rules for activity codes and floors climbed. Activity-conditioned Poisson subsampling emulates device-driven irregular timestamps. We benchmark HHMM against CTGAN and TVAE using a synthetic IEEE BHI dataset and validate on a real Fitbit dataset. Fidelity is assessed by comparing distributions of pairwise inter-record distances-within synthetic vs. within reference cohorts-via Wasserstein distances for Kolmogorov-Smirnov (KS), Jensen-Shannon (JSD), and distance-correlation (DC) metrics. Results: On the BHI dataset, mean WD(KS)/WD(JSD)/WD(DC) were 0.125/0.242/0.327 for HHMM, 0.130/0.245/0.257 for CTGAN, and 0.129/0.246/0.249 for TVAE. On the Fitbit dataset, values were 0.295/0.489/0.233 (HHMM), 0.293/0.488/0.209 (CTGAN), and 0.293/0.588/0.160 (TVAE). Discussion: HHMM offers task-specific gains on synthetic benchmarks. Real-world results highlight a need for domain adaptation. The method is computationally efficient and privacy-preserving, supporting scalable synthetic data generation for wearable health research.
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