Multi-source partial domain adaptation with Gaussian-based dual-level weighting for PPG-based heart rate estimation

• We propose a novel GAussian-based dUaL-level weighting (GAUL) for photoplethysmography (PPG)-based heart rate estimation, formulating it as a multi-source partial domain adaptation (MSPDA) problem. • GAUL applies dual-level weighting using a Gaussin function at both the domain and samples levels to filter out irrelevant source data, improving adaptation. • GAUL enhances heart rate estimation performance for new subjects without the need for additional labeled data, validated across three benchmark datasets. • GAUL provides a potential solution for wearable device applications, enabling personalized heart rate estimation in scenarios where collecting labeled PPG data is impractical. Photoplethysmography (PPG) signals from wearable devices have expanded the accessibility of heart rate estimation. Recent advances in deep learning have significantly improved the generalizability of heart rate estimation from PPG signals. However, these models exhibit performance degradation when used for new subjects with different PPG distributions. Although previous studies have attempted subject-specific training and fine-tuning techniques, they require labeled data for each new subject, limiting their practicality. In response, we explore the application of domain adaptation techniques using only unlabeled PPG signals from the target subject. However, naive domain adaptation approaches do not adequately account for the variability in PPG signals among different subjects in the training dataset. Furthermore, they overlook the possibility that the heart rate range of the target subject may only partially overlap with that of the source subjects. To address these limitations, we propose a novel multi-source partial domain adaptation method, GAussian-based dUaL-level weighting (GAUL), designed for the PPG-based heart rate estimation, formulated as a regression task. GAUL considers and adjusts the contribution of relevant source data at the domain and sample levels during domain adaptation. The experimental results on three benchmark datasets demonstrate that our method outperforms existing domain adaptation approaches, enhancing the heart rate estimation accuracy for new subjects without requiring additional labeled data. The code is available at: https://github.com/Im-JihyunKim/GAUL .