Predicting Out-of-Hospital Vital Sign Measurements through Deep Learning

People may want to monitor their health conditions while they conduct their daily activities. For this, they need solutions that perform continuous health monitoring. Today, most existing solutions in the market (e.g., Apple watch, SpO2 ring) only report current vital sign measurements; they do not predict future values of those vital signs. We developed two predictive models that give people (patients or non-patients) predictions regarding the measurements of two critical vital signs, namely, heart rate and Oxygen saturation level. Our experimental results indicate that both models have a good accuracy.