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arXiv · 2507.14185

Latent Sensor Fusion: Multimedia Learning of Physiological Signals for Resource-Constrained Devices

Abdullah Ahmed·Jeremy Gummeson
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Abstract

Latent spaces offer an efficient and effective means of summarizing data while implicitly preserving meta-information through relational encoding. We leverage these meta-embeddings to develop a modality-agnostic, unified encoder. Our method employs sensor-latent fusion to analyze and correlate multimodal physiological signals. Using a compressed sensing approach with autoencoder-based latent space fusion, we address the computational challenges of biosignal analysis on resource-constrained devices. Experimental results show that our unified encoder is significantly faster, lighter, and more scalable than modality-specific alternatives, without compromising representational accuracy.

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Abdullah Ahmed, Jeremy Gummeson. 2025-07-13. Latent Sensor Fusion: Multimedia Learning of Physiological Signals for Resource-Constrained Devices. https://doi.org/10.1145/3731715.3733482

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