Researchers develop SOTER, a generative foundation model for wearable human physiological signals, addressing the challenges of multichannel, irregularly sampled, and noisy data. SOTER combines spatial feature-aware backbones, spectrum-guided expert specialization, and continuous-time latent evolution. The model is pre-trained on 226 billion time points from five public datasets and achieves state-of-the-art results in zero-shot forecasting, classification, and imputation tasks.