HARP: Learning Heterogeneity-Aware Representations for Physiological Health Sensing

Health-sensing data supporting classification of clinical and everyday health states are naturally heterogeneous, where they differ in sensor sets, temporal resolutions, and missingness patterns across datasets. Despite advances in time-series pretraining, existing models mainly expect that target data is complete and regularly sampled. The transfer across real-world heterogeneous sensing configurations and the adaptation without target outcome labels remain challenging. We introduce HARP, a pretrained framework for health classification across heterogeneous sensing configurations. HARP uses masked-reconstruction pretraining over 50k participants with diverse sensor sets and observation length to enable learning from incomplete patterns. By combining physical duration patch embedding with canonical variable embedding, consistent temporal and variable meaning across datasets are preserved. For downstream classification, HARP supports both supervised adaptation and zero-shot transfer by a lightweight sensor-language bridge when target labels are unavailable. Across six clinical and wearable heterogeneous sensing dataests, HARP consistently outperforms state-of-the-art pretrained time-series models, improving average performance over the best pretrained model by 12% under linear probing and 10.3% under zero-shot transfer, while achieving competitive performance on three regularly sampled benchmarks. Beyond predictive performance, interpretability and scaling analyses show that HARP preserves temporal and variable meaning across various configurations and benefits from additional unlabeled pretraining data, supporting the learning of reusable heterogeneity-aware representations for real-world physiological sensing.