SBIR Phase II: Development of motion artifact correction systems for ViTrack: an accurate, continuous, and non-invasive blood pressure monitor

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to meet the urgent need for a wearable blood pressure (BP) monitor for accurate and continuous BP measurements. The current standard for arm cuff-based BP assessments is unreliable and limited to single time-point BP estimates, but an individual’s BP fluctuates significantly over 24 hours. In US hospitals, 35 million patients are admitted annually for critical illness and/or undergo major surgeries that require BP monitoring. Inaccurate, single-point, cuff-based BP monitoring during surgery leads to unrecognized low BP and contributes to 14 million heart attacks and kidney failures annually. ViTrack is a wrist-wearable device that utilizes a proprietary optomechanical sensor array and computer vision to accurately and continuously measure radial artery BP without requiring external calibration. However, continuously measuring BP in clinical settings is challenging due to artifacts from patient movements. In NSF Phase 1, ViTrack's sensor array was used to develop an advanced computer vision technology for motion artifact correction. Other than the hospital market, the multi-billion dollar market segments for an accurate and continuous BP monitor include remote patient monitoring for 104 million Americans with high BP and the consumer wellness markets. The objective of this Small Business Innovation Research Phase II project is to improve computer vision technology for motion correction and make it suitable for use in various hospital settings with different types of motion artifacts. The project will begin by improving the effectiveness and adaptability of computer vision-based algorithms through testing and optimization using a wide range of patient data. Artificial intelligence (AI) will then be integrated to further improve the algorithms' ability to handle various motion artifacts. In order to achieve this goal, we will create thousands of synthetic 3D-rendered videos using various combinations of real-world ViTrack data, both with and without motion artifacts. These videos will then be utilized to train a specific type of AI algorithm known as a deep neural network, using a modern approach called a vision transformer. The main objective of this training is to enable the algorithm to recognize and address motion artifacts without compromising the physiological signal. After developing, we will test the algorithms' accuracy in providing continuous BP readings across a variety of clinical data. Once validated, the algorithms will be integrated into the ViTrack device through a software module following FDA guidelines for good machine learning practices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.