CADF: Real-time multi-biosignal modal recognition with causality-aware dimension fusion

Abstract

Real-time analysis of multiple biological signals offers social media systems valuable insights into user engagement, but capturing the complex temporal dynamics and inter-signal relationships remains a challenge. This study introduces a novel framework, CADF (Causality-Aware Dimension Fusion), for real-time multi-biosignal modality recognition. CADF introduces a causality-aware temporal encoder that preserves temporal causality while effectively modeling long-term dependencies in one-dimensional signals. Additionally, the time series data is converted to extract 2D spatial masks. The bi-dimensional features are fused to identify modalities with the aid of a streamlined MultiHead mechanism. Extensive experiments on the DSADS, WESAD, and CAP datasets show that CADF reduces the number of parameters by at least 58% and improves the accuracy by 8% compared to the SOTA model. In particular, the accuracy of the three-classification emotion recognition task reached 95%. These results emphasize the effectiveness and efficiency of CADF in real-time biosignal analysis, with important implications for user-centric applications.