Robust Heart Rate Detection via Multi-Site Photoplethysmography

TL;DR

This paper presents a sensor fusion approach that combines multiple PPG signals from various body sites to improve heart rate estimation accuracy during real-world activities, outperforming single-site methods.

Contribution

The study introduces a novel multi-site sensor fusion technique that adaptively integrates PPG signals based on quality, enhancing heart rate detection in everyday environments.

Findings

Achieves a mean HR error of 2.4 bpm, 46% lower than the best single-site device.

Demonstrates robustness of the method during diverse real-world activities.

Validates effectiveness on a novel multi-site PPG and ECG dataset from 14 participants.

Abstract

Smartwatches have become popular for monitoring physiological parameters outside clinical settings. Using reflective photoplethysmography (PPG) sensors, such watches can non-invasively estimate heart rate (HR) in everyday environments and throughout a patient's day. However, achieving consistently high accuracy remains challenging, particularly during moments of increased motion or due to varying device placement. In this paper, we introduce a novel sensor fusion method for estimating HR that flexibly combines samples from multiple PPG sensors placed across the patient's body, including wrist, ankle, head, and sternum (chest). Our method first estimates signal quality across all inputs to dynamically integrate them into a joint and robust PPG signal for HR estimation. We evaluate our method on a novel dataset of PPG and ECG recordings from 14 participants who engaged in real-world activities outside the laboratory over the course of a whole day. Our method achieves a mean HR error of 2.4\,bpm, which is 46\% lower than the mean error of the best-performing single device (4.4\,bpm, head).