Explainable Liquid Time-Constant Network for Multi-Modal Fatigue Detection in Healthcare 4.0.

Abstract

Driver fatigue detection has become increasingly critical for healthcare 4.0 systems, as it enables real-time monitoring of internal cognitive states to ensure road safety. However, existing methods often suffer from two critical limitations, i.e., insufficient modeling of time-varying dynamics, and ineffective fusion of multi-modal signals due to neglect of intra- and inter-modality dependencies. To address these challenges, we propose an explainable AI (XAI) framework, named LTC-DFD, for multi-modal driving fatigue detection. It is composed of five parallel branches to process distinct physiological modalities, each equipped with a Liquid Time-Constant block to model temporal dynamics using trainable differential equations. A dual-level attention mechanism is introduced, combining channel attention to emphasize salient intra-modal features and token-level attention to capture cross-modal dependencies. The fused representation is then passed through a fully connected regression head to estimate the driver's fatigue level. We evaluate LTC-DFD on the SEED-VIG dataset under a cross-subject protocol. It achieves an accuracy of 96.5%, RMSE of 0.22, and parameter count of only 0.42 M, demonstrating superior performance over existing state-of-the-art (SOTA) methods. In addition, the learned temporal dynamics and attention patterns are consistent with known neurophysiological markers of drowsiness, supporting trustworthy deployment of LTC-DFD in healthcare 4.0 driver-monitoring services.