A feature-based approach on contact-less blood pressure estimation from video data

Conventional blood pressure monitors and sensors have several limitations in terms of accuracy, measurement time, comfort or safety. To address these limitations, we realized and tested a surrogate-based contact-less blood pressure estimation method which relies on a single remote photoplethysmogram (rPPG) captured by camera. From this rPPG signal, we compute 120 features, and perform a sequential forward feature selection to obtain the best subset of features. With a multilayer perceptron model, we obtain a mean absolute error ± standard deviation of MAE 5.50±4.52 mmHg for systolic pressure and 3.73±2.86 mmHg for diastolic pressure. In contrast to previous studies, our model is trained and tested on a data set including normotensive, pre-hypertensive and hypertensive values.