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Optical Bio-Sensing Laboratory

Texas A&M University College of Engineering

Integrated Multimodal Chest Patch for Continuous Cardiovascular and Hemodynamic Monitoring

Early multimodal chest-mounted cardiovascular monitoring prototypes demonstrating system evolution from a large, tethered round sensing platform (black) to a compact, fully wireless wearable patch (white). This transition reflects significant advancements in system integration, user comfort, real-world deployability, and overall technology readiness level (TRL), enabling continuous ambulatory physiological monitoring outside controlled laboratory environments.

This technology enables continuous, noninvasive cardiovascular monitoring through a compact, chest-mounted multimodal patch system. The platform integrates three complementary sensing modalities, photoplethysmography (PPG), electrocardiography (ECG), and strain-gauge-based mechanical sensing, to capture a comprehensive representation of cardiovascular dynamics.
The initial prototype consists of a tethered, round multimodal patch that incorporates all sensing components. This device simultaneously acquires electrical cardiac activity (ECG), optical blood volume changes (PPG), and local mechanical deformation using strain-gauge technology. Together, these signals provide synchronized insight into cardiac timing, vascular response, and pressure-related waveform morphology.
A second-generation device advances this design into a compact, fully untethered patch. This version emphasizes improved wearability, reduced form factor, and enhanced user comfort while maintaining multimodal sensing capability. Its wireless architecture enables greater mobility and supports real-world, ambulatory monitoring without the constraints of external wiring.
By combining electrical, optical, and mechanical measurements, the system enables robust characterization of cardiovascular function. The multimodal approach improves resilience to motion artifacts and signal noise by allowing cross-validation between sensing channels. This platform supports the development of advanced algorithms to estimate physiological parameters, including heart rate, blood pressure trends, and hemodynamic changes, under both static and dynamic conditions.

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