HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment
cs.AI
Submitted: 2026-10-10
Updated: 2026-10-10
Terminology
Sources
- Large-scale Training of Foundation Models for Wearable Biosignals
- Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
- Subject-Aware Contrastive Learning for Biosignals
- SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals
- Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
- QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients
- Masked Autoencoders Are Scalable Vision Learners
- GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
- Frozen Language Model Helps ECG Zero-Shot Learning
- Scaling Wearable Foundation Models
- PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
- PaPaGei: Open Foundation Models for Optical Physiological Signals
- From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
- SensorLM: Learning the Language of Wearable Sensors
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