A Neuro-Symbolic System for Interpretable Multimodal Physiological Signals Integration in Human Fatigue Detection
We propose a neuro-symbolic architecture that learns four interpretable physiological concepts, oculomotor dynamics, gaze stability, prefrontal hemodynamics, and multimodal, from eye-tracking and neural hemodynamics, functional near-infrared spectroscopy, (fNIRS) windows using attention-based encoders, and combines them with differentiable approximate reasoning rules using learned weights and soft thresholds, to address both rigid hand-crafted rules and the lack of subject-level alignment diagnostics. We apply this system to fatigue classification from multimodal physiological signals, a domain that requires models that are accurate and interpretable, with internal reasoning that can be i...
| Authors | Mohammadreza Jamalifard, Yaxiong Lei, Parasto Azizinezhad et al. |
|---|---|
| Primary category | cs.HC |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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