PPG-Sport: A Dataset for Reliable Heart Rate Monitoring from Wrist PPG Under Dynamic Sports Conditions

Abstract

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and basketball, amounting to 48 hours of multimodal data. PPG-Sport uniquely captures three critical properties absent in prior datasets: (1) non-periodicity, reflecting irregular and broadband motion patterns; (2) high intensity, with frequent, large-magnitude accelerations; and (3) bilateral asymmetry, caused by distinct functional roles of the dominant and non-dominant hands during sports. We further establish a deep-learning-based benchmark that combines both temporal and spectral representations of PPG and IMU signals to evaluate heart rate estimation performance. Experimental results show that models trained only on conventional periodic activities fail drastically in sports scenarios. Although incorporating sports data mitigates the degradation, significant errors remain. These findings highlight PPG-Sport as an essential and challenging benchmark for developing motion-robust physiological sensing algorithms during sports activities. Dataset and benchmark code are available at: https://github.com/LaserHu/PPG-Sport.