[2604.04297] PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence

Computer Science > Artificial Intelligence arXiv:2604.04297 (cs) [Submitted on 5 Apr 2026] Title:PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence Authors:Marija Zelic, Anna Tegon, Yawei Li, Thorir Mar Ingolfsson, Luca Benini View a PDF of the paper titled PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence, by Marija Zelic and 4 other authors View PDF HTML (experimental) Abstract:Physiological foundation models (FMs) have shown promise for biosignal representation learning, yet most remain confined to a single modality such as EEG, ECG, or PPG, largely because paired multimodal datasets are scarce. In this paper, we present PanLUNA, a compact 5.4M-parameter pan-modal FM that jointly processes EEG, ECG, and PPG within a single shared encoder. Extending LUNA's channel-unification module, PanLUNA treats multimodal channels as entries in a unified query set augmented with sensor-type embeddings, enabling efficient cross-modal early fusion while remaining inherently robust to missing modalities at inference time. Despite its small footprint, PanLUNA matches or exceeds models up to 57$\times$ larger: 81.21% balanced accuracy on TUAB abnormal EEG detection and state-of-the-art 0.7416 balanced accuracy on HMC multimodal sleep staging. Quantization-aware training with INT8 weights recovers $\geq$96% of full-precision performance, and deployment on the GAP9 ultra-low-power RISC-V microcontroll...