DT4ECG: a subject-aware ECG monitoring framework for joint user attribution and human activity recognition
Year: 2026
Type: article
Abstract: 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.
Authors: Siyu You, Boyuan Gu, Yanhui Yang, Shuaiqi Cheng, Shiyu Yu, Shisheng Guo
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4407690041