Abstract Continuous blood pressure (BP) is one of the most critical monitoring parameters during anesthesia, surgery and in intensive care units (ICU). Both hypotension and hypertension can impair the function of vital organs (e.g. brain, heart and kidneys), and intraoperative hypotension is associated with postoperative mortality, which makes it important to detect BP changes as quickly as possible to prompt timely intervention or therapy. However, the current gold standard technology for BP monitoring, an invasive arterial line (a-line), causes patient suffering (physical pain) and increases the risk of infection. In the United States, about 80,000 blood stream infections caused by an arterial catheter are reported annually. Due to the inherent risks associated with a-line, it is used only for clinically indicated high risk surgeries or ICU patients. As a result of the a-line risks and discomforts, even though more than 300 million surgeries are performed worldwide each year, only a small portion receive continuous BP monitoring. In addition, although vital sign (ECG, pulse oximetry, BP etc) monitoring is routine in surgical rooms and ICUs, currently most monitoring devices are fixed in individual rooms, which result in gaps in patient monitoring, accidents during patient transport process, and extra work to disconnect and reconnect sensors when leaving and entering a new facility. Seamless “continuum of care” monitoring—for instance from surgical room to ICUs, including transport in between and without reconnecting sensors—is on top of the wish list by clinician. In recent years, efforts have been made to develop portable ECG monitors and “mobile ICUs”; however so far, no continuous and seamless BP monitoring has been achieved. This proposal fully leverages the outcomes from the related R21 (EB022271) project. We will develop novel machine learning and deep learning based data fusion algorithms to use existing vital signs for continuous BP monitoring, then integrate them with our unique wearable patient monitoring system to form a novel perioperative patient monitoring system. We will test the system’s performance against gold standard a- line and Finapres BP technologies. to develop a fully functional technology for noninvasive, continuous, and seamless BP monitoring. We will also develop a public database for future BP technology development. The proposed multimodality algorithms, seamless BP monitoring system and PhysioNet database will provide major steps forward to meet the clinical need for noninvasive continuous BP monitoring.