Development of a Machine Learning Algorithm for Estimating Vital Parameters using a Smart Medical Watch

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VitalTracer Ltd., which operates in Ottawa and Montreal, has developed a smart medical watch, VTLAB, which is designed to be capable of continuously measuring vital physiological parameters such as blood pressure (BP), heart rate (HR), blood oxygen saturation (SpO2), body temperature, respiratory rate, ECG (electrocardiogram), PPG (photoplethysmogram), steps and motion. This array of data is then used for predicting and managing medical conditions. VitalTracer would like to enhance their current version of VTLAB by improving their current AI (artificial intelligence) algorithms for analyzing continuous physiological data. Centennial College's Wearable, Interactive and Mobile Technology Access Center in Health (WIMTACH) and VitalTracer will collaborate through this Engage project, to conduct a systematic investigation into novel machine learning and feature engineering algorithms and develop a novel machine learning algorithm which analyzes VTLAB data captured with motion-artifacts and estimates vital parameters such as blood pressure. As an outcome of this project, the WIMTACH team will deliver a signal processing and machine learning software application that would seamlessly analyze, measure and predict vital body parameters using the VTLAB medical watch.