Hierarchical heated markov modeling for synthesizing activity data from wearable devices
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.
Authors
- Dan Ruan (ORCID: https://orcid.org/0000-0003-3400-7684)
- Duo Fang (ORCID: https://orcid.org/0009-0004-2692-5199)
Institutions
- University of California, Los Angeles (US)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1016/j.csbj.2025.11.053
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00