Cross-Modal Deep Hashing Framework for Wearable PPG Signal Retrieval with Self-Supervised Semantic Representation Learning
Keywords:
deep hashing, photoplethysmography, self-supervised learning, cross-modal retrieval, wearable computing, semantic representationAbstract
The proliferation of wearable photoplethysmography sensors has generated vast streams of cardiovascular data, creating an urgent need for efficient, semantic-aware retrieval mechanisms that can operate across heterogeneous contextual modalities. This paper presents a cross-modal deep hashing framework designed for PPG signal retrieval that integrates self-supervised semantic representation learning to extract robust, modality-invariant features from unlabeled physiological time series and associated metadata. The framework maps PPG segments and their corresponding semantic descriptors into a shared binary Hamming space, enabling fast approximate nearest neighbor search while preserving clinically meaningful similarities. A comprehensive system-level analysis is conducted, addressing architectural choices that balance quantization error against retrieval precision, the integration of contrastive and masked reconstruction objectives for representation learning, and the trade-offs inherent in deploying such models on resource-constrained wearable edge devices. The discussion extends to governance and policy considerations, including data privacy, fairness across demographic groups, and the sustainability of large-scale health retrieval infrastructures. By emphasizing structural robustness, bias mitigation, and cross-modal alignment, the proposed framework offers a principled pathway toward scalable, privacy-preserving, and equitable health monitoring systems. The paper concludes with an examination of deployment scenarios, evaluation benchmarks, and future directions for cross-modal biosignal retrieval in real-world healthcare ecosystems.
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