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

• URL: http://arxiv.org/abs/2507.14185v1
• Date: Sun, 13 Jul 2025 02:58:48 GMT
• Title: Latent Sensor Fusion: Multimedia Learning of Physiological Signals for Resource-Constrained Devices
• Authors: Abdullah Ahmed, Jeremy Gummeson,
• Abstract summary: We leverage latent spaces to develop a modality-agnostic, unified encoder.<n>Our method employs sensor-latent fusion to analyze and correlate physiological signals.
• Score: 1.7275975642409687
• License: http://creativecommons.org/licenses/by/4.0/
• 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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