PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning

Summary

Singapore Management University / Cambridge team aggregates 16 public PPG datasets (clinical, lab, and in-the-wild — fingertip, ear, and wrist) into a single harmonized 10-second/125Hz format, then converts existing labels (HR, HRV, BP category, SQI, stress/affect, sleep-disordered breathing, AF, arrhythmia, SpO2, RR) into ~2.5 million natural-language question-answer pairs across 12 task categories. This is a dataset-and-benchmark paper, not a new pretraining method — no novel encoder or SSL objective is proposed. Baseline results use existing multimodal PPG-aware LLMs (PPG encoder + LoRA fine-tuned LLaMA/Qwen) on the QA format.