Leveraging Shared Prototypes for a Multimodal Pulse Motion Foundation Model

• URL: http://arxiv.org/abs/2510.09764v1
• Date: Fri, 10 Oct 2025 18:13:38 GMT
• Title: Leveraging Shared Prototypes for a Multimodal Pulse Motion Foundation Model
• Authors: Wanting Mao, Maxwell A Xu, Harish Haresamudram, Mithun Saha, Santosh Kumar, James Matthew Rehg,
• Abstract summary: ProtoMM is a novel framework that introduces a shared prototype dictionary to anchor heterogeneous modalities in a common embedding space.<n>By clustering representations around shared prototypes rather than explicit negative sampling, our method captures complementary information across modalities and provides a coherent "common language" for physiological signals.
• Score: 4.895784700544358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes. While recent self-supervised learning (SSL) advances have improved unimodal representation learning, existing multi-modal approaches often rely on CLIP-style contrastive objectives that overfit to easily aligned features and misclassify valid cross-modal relationships as negatives, resulting in fragmented and non-generalizable embeddings. To overcome these limitations, we propose ProtoMM, a novel SSL framework that introduces a shared prototype dictionary to anchor heterogeneous modalities in a common embedding space. By clustering representations around shared prototypes rather than explicit negative sampling, our method captures complementary information across modalities and provides a coherent "common language" for physiological signals. In this work, we focus on developing a Pulse Motion foundation model with ProtoMM and demonstrate that our approach outperforms contrastive-only and prior multimodal SSL methods, achieving state-of-the-art performance while offering improved interpretability of learned features.

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