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arXiv Machine Learning By Xu Wang, Shuhan Yi, Zhen Cheng

An Agent Architecture with Tools and Memory for Selective Correction of Wrist PPG Heart Rate Estimates

Read the original on arXiv Machine Learning →

The paper introduces an agent architecture that uses tools and memory to selectively correct wrist photoplethysmography (PPG) heart‑rate estimates. By generating candidate corrections from pulse, motion, and historical data, and evaluating their predicted benefits and harms, the system decides whether to retain, correct, or reject each estimate. Across four public datasets, the approach reduced mean absolute error by up to 44.5% and demonstrated real‑time deployment on a wearable‑smartphone platform.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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