Key result
Two novel algorithms enhance earbud-based BCG signal assessment for HRV estimation over existing methods.
Why the study?
Earbud-based ballistocardiogram assessment is prone to orientation misalignment, causing significant variations in signal morphology unlike traditional head-to-foot axis measurements.
Do novel calibration and calibration-free algorithms improve heart rate variability estimation and morphological feature extraction from earbud-based ballistocardiograms compared to existing methods?
Do novel calibration and calibration-free algorithms improve heart rate variability estimation and morphological feature extraction from earbud-based ballistocardiograms compared to existing methods?
Novel calibration and calibration-free algorithms for earbud-based ballistocardiograms aim to improve signal quality and heart rate variability estimation by addressing orientation misalignment.
May enhance earbud BCG for ambulatory HRV monitoring; leaves open prospective clinical validation.
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.
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Li et al. (2025) studied this question. Calibration and calibration-free axis fusion algorithms for earbud-based BCG vs. Existing methods was evaluated on Heart rate variability (HRV) estimation and morphological feature extraction. Two novel algorithms (sensor-to-body-segment calibration and calibration-free axis fusion) were proposed to enhance earbud-based ballistocardiogram signal assessment against existing methods.
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