DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection

TL;DR

DT4ECG is a dual-task deep learning framework that effectively recognizes human identity and detects activities from ECG signals, using advanced attention mechanisms and gradient balancing to improve accuracy and robustness.

Contribution

The paper introduces a novel dual-task learning framework with a Sequence Channel Attention mechanism and GradNorm for balanced multi-task training in ECG-based recognition.

Findings

Achieved 99.12% accuracy in identity recognition

Achieved 90.11% accuracy in activity detection

Demonstrated robustness and potential for real-world applications

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.