STaRC: Spectral–Temporal Cross-Prediction and Bayesian Relational Calibration for Cross-Dataset Few-Shot Activity Recognition

Cross-dataset few-shot human activity recognition (HAR) requires learning transferable motion representations across heterogeneous sensing conditions while adapting to target activities from few labeled examples. Existing approaches often treat cross-dataset representation learning and few-shot adaptation separately, leaving uncertainty in transferring heterogeneous base knowledge under scarce target supervision insufficiently modeled. We propose STaRC, a two-stage framework that integrates Spectral–Temporal Cross-Prediction (STCP) with Bayesian Relational Calibration (BRC). We harmonize ten public IMU datasets into a common input format for self-supervised pretraining without requiring aligned activity labels. The resulting harmonized multi-dataset collection will be publicly released. STCP learns transferable representations through bidirectional cross-prediction between temporal and spectral views, capturing complementary temporal dynamics and spectral regularities beyond direct cross-view alignment. During few-shot adaptation, BRC probabilistically models relations between target features and base-class prototypes, samples multiple relational graphs to calibrate target representations, and integrates predictions from multiple base-knowledge experts through task-conditioned routing. This component transfers heterogeneous base knowledge while accounting for relational uncertainty induced by cross-dataset shift and limited supervision. Experiments across target datasets and shot settings demonstrate the effectiveness of both STCP and BRC. Under the same downstream calibration protocol, comparisons with representative self-supervised methods show the benefits of STCP pretraining. Comparisons with competitive few-shot baselines further support the effectiveness of BRC in transferring base knowledge to target episodes, particularly in low-shot settings. Ablation studies examine the effects of pretraining data scale, temporal and spectral learning objectives, and Bayesian calibration compared with point-estimate or no calibration. These results suggest that predictive representation learning and uncertainty-aware relational calibration provide a complementary approach to data-efficient cross-dataset HAR, with potential for personalized wearable sensing.