Cardiac Output (CO) is a critical indicator of health, offering insights into cardiac dysfunction, acute stress responses, and cognitive decline. Traditional CO monitoring methods, like impedance cardiography, are invasive and impractical for daily use, leading to a gap in continuous, non-invasive monitoring. Although recent advancements explored wearables on heart rate monitoring, these approaches face challenges in accurately estimating CO due to the indirect nature of the signals. To address these challenges, we introduce EarCO, a non-invasive multimodal CO monitoring system with Photoplethysmography and Ballistocardiogram signals on commodity earbuds. A novel feature fusion method is proposed to integrate raw signals and prior knowledge from both modalities, improving the system’s interpretability and accuracy. EarCO achieves an error of 1.080 L/min in the leave-one-subject-out settings with 62 subjects, making cardiovascular health monitoring accessible and practical for daily use.

Know Your Heart Better: Multimodal Cardiac Output Monitoring using Earbuds

Earbuds are instrumental in health monitoring but the orientation can variate among users, which may significantly impact the health-monitoring system generalizability. To study the effect of earbuds orientation heterogeneity and align kinematics across earbuds orientations, we collected a dataset with various rotations relative to a baseline orientation. We developed the coordinate transformation by estimating Euler angles in transformation matrices with either grid search or Markov Chain Monte Carlo (MCMC) sampling. Taking ~ 17 seconds with a personal laptop, the MCMC method accurately estimated the coordinate transformation matrices to enable the transformed tri-axial linear acceleration to better match the baseline tri-axial linear acceleration with an average relative error of 1.899% (0.186 m/s2) and a maximum relative error of 2.774% averaged over all test orientations. Using the estimated transformation matrices and Samsung dataset of identification of activities of daily living (ADL), we validated the statistically significant impact of earbuds orientation heterogeneity on ADL identification (p < 0.001), which can cause 14.0% reduction in mean accuracy and 18.7% reduction in mean macro-average F1-score. To sum up, the MCMC method developed can be applied in earbuds kinematics alignment to address orientation heterogeneity and enable better earbuds-based health monitoring.

Earbuds Orientation Alignment Based on Markov Chain Monte Carlo Sampling

Abstract Emotions are inherently complex – situated inside the brain while being influenced by conditions inside the body and outside in the world – resulting in substantial variation in experience. Most studies, however, are not designed to sufficiently sample this variation. In this paper, we discuss what could be discovered if emotion were systematically studied within persons ‘in the wild’, using biologically-triggered experience sampling: a multimodal and deeply idiographic approach to ambulatory sensing that links body and mind across contexts and over time. We outline the rationale for this approach, discuss challenges to its implementation and widespread adoption, and set out opportunities for innovation afforded by emerging technologies. Implementing these innovations will enrich method and theory at the frontier of affective science, propelling the contextually situated study of emotion into the future. 

Multimodal, Idiographic Ambulatory Sensing Will Transform our Understanding of Emotion

Continuous stress exposure negatively impacts mental and physical well-being. Physiological arousal due to stress affects heartbeat frequency, changes breathing pattern and peripheral temperature, among several other bodily responses. Traditionally stress detection is performed by collecting signals such as electrocardiogram (ECG), respiration, and skin conductance response using uncomfortable sensors such as a chestband. In this study, we use earbuds that passively measure photoplethysmography (PPG), core body temperature, and inertial measurements. We have conducted a lab study exposing 18 participants to an evaluated speech task and additional tasks aimed at increasing stress or promoting relaxation. We simultaneously collected PPG, ECG, impedance cardiography (ICG), and blood pressure using laboratory grade equipment as reference measurements. We show that the earbud PPG sensor can reliably capture heart rate and heart rate variability. We further show that earbud signals can be used to classify the physiological responses associated with stress with 91.30% recall, 80.52% precision, and 85.12% F1-score using a random forest classifier with leave-one-subject-out cross-validation. The accuracy can further be improved through multi-modal sensing. These findings demonstrate the feasibility of using earbuds for passively monitoring users' physiological responses.

Detecting Physiological Responses Using Multimodal Earbud Sensors

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Detecting Physiological Responses Using Multimodal Earbud Sensors