DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection
Journal:
arXiv
Published Date:
Feb 16, 2025
Abstract
This article introduces DT4ECG, an innovative dual-task learning framework
for Electrocardiogram (ECG)-based human identity recognition and activity
detection. The framework employs a robust one-dimensional convolutional neural
network (1D-CNN) backbone integrated with residual blocks to extract
discriminative ECG features. To enhance feature representation, we propose a
novel Sequence Channel Attention (SCA) mechanism, which combines channel-wise
and sequential context attention to prioritize informative features across both
temporal and channel dimensions. Furthermore, to address gradient imbalance in
multi-task learning, we integrate GradNorm, a technique that dynamically
adjusts loss weights based on gradient magnitudes, ensuring balanced training
across tasks. Experimental results demonstrate the superior performance of our
model, achieving accuracy rates of 99.12% in ID classification and 90.11% in
activity classification. These findings underscore the potential of the DT4ECG
framework in enhancing security and user experience across various applications
such as fitness monitoring and personalized healthcare, thereby presenting a
transformative approach to integrating ECG-based biometrics in everyday
technologies.
