Wearable diagnosis stops treating every sensor stream alike: a gated capsule-Transformer lifts accuracy to 0.9348
For clinical diagnosis from multi-device wearable sensors, a cross-platform framework chains stream selection, class-imbalance correction and spatiotemporal feature learning into one pipeline. Previously, sensor modalities, sampling rates and transmission properties were handled separately, rare abnormal samples were scarce, and inference shifted with the device. On two public datasets the framework reaches AUC 0.966 and 0.947; at 24 streams it reports accuracy 0.9348, precision 0.9305, sensitivity 0.9615 and false-positive rate 0.0915.