DT4ECG: a subject-aware ECG monitoring framework for joint user attribution and human activity recognition

Electrocardiogram (ECG) signals contain information about both user identity and physical activity, but individual physiological differences complicate activity inference. We propose DT4ECG, a subject-aware multi-task framework for joint identity recognition (IDR) and human activity recognition (HAR) among enrolled users. A Multi-Scale Residual Convolution (MSRC) backbone extracts complementary waveform and temporal features, while a Subject Adaptive Fusion Module (SAFM) uses learned identity-related representations to calibrate activity features. Evaluation on two public datasets and one self-built dataset achieves IDR macro F1 of 98.59--99.96\% and HAR macro F1 of 95.40--96.82\%. Ablation results show consistent HAR improvements from SAFM across the three datasets, and recorded-noise tests characterize sensitivity to common ECG artifacts. The two-channel model contains 0.361 million parameters. These findings demonstrate the value of treating subject-related physiology as context for activity recognition. By linking activity estimates to user attribution within a single ECG framework, this work provides a basis for integrated physiological monitoring among enrolled users in shared-device settings.