Clinical Evaluation of a Multimodal On-Body Sensor Array

Continuous, noninvasive blood pressure monitoring remains an unmet clinical need, particularly in the intensive care unit (ICU) where hemodynamically unstable patients need high-frequency monitoring. Invasive arterial catheterization represents the current standard of care for continuous blood pressure (BP) monitoring, but it carries risks and limits patient mobility. In this study, we evaluate the MOSAIC system, a novel multi-modal, multi-nodal wearable, wireless sensor system placed on multiple locations on the body, for continuous noninvasive BP estimation in a cohort of ICU patients. Unlike existing continuous BP sensors, the MOSAIC system offers an ideal form factor for continuous BP monitoring, enabling a fully untethered setup which minimally impacts activities of daily living. Leveraging sensor-derived biosignals to compute continuous BP, we determine the accuracy of our BP regression models using arterial line-derived blood pressure reading as a ground truth. Using a Light gradient boosted machine (LGBM)-based regression model, we demonstrate strong beat-to-beat agreement with a mean absolute error (MAE) of 5.66 ± 5.94 mmHg for systolic BP (SBP) prediction and 2.45 ± 2.87 mmHg for diastolic BP (DBP) prediction, and average ratio variability (ARV) of 0.527 ± 0.185 and 0.489 ± 0.170 for SBP and DBP, respectively, compared to linear and deep-learning regression baselines. Our findings demonstrate strong agreement between the predicted BP values and invasive, arterial-line BP measurements, supporting the feasibility of wearable, wireless, and cuffless blood pressure monitoring in high-acuity clinical settings.