Optimizing Biomarkers from Earbud Ballistocardiogram: Calibration and Calibration-Free Algorithms for Accelerometer Axis Selection and Fusion

The earbud-based ballistocardiogram (BCG) assessment holds significant promise for monitoring diverse physiological signals, including stress, cardiac activity, and blood pressure. However, unlike traditional methods that measure the force component along the head-to-foot axis for enhanced BCG signal quality, ear-worn devices are prone to orientation misalignment, leading to significant variations in BCG morphology. To address this challenge, we propose two novel algorithms: one that employs sensor-to-body-segment calibration and another that applies a calibration-free, physiologically informed axis fusion method to enhance earbud-based BCG signal assessment. We evaluate the performance of these approaches against existing methods, focusing on heart rate variability (HRV) estimation and morphological feature extraction. Through a comprehensive investigation, we aim to identify optimal strategies for obtaining high-quality BCG signals using ear-worn devices.