论文信息 - Machine Learning Driven Heart Rate Detection with Camera Photoplethysmography in Time Domain

Machine Learning Driven Heart Rate Detection with Camera Photoplethysmography in Time Domain

Measuring bio signals such as the heart rate in non medical applications is gaining an increasing importance. With camera based photoplethysmography (PPG) it is possible to measure the heart rate remotely with built in webcams of every tablet and laptop. Recent research with machine learning based methods showed great success compared to signal processing based methods. In this paper, we use k-nearest neighbor (kNN) and multilayer perceptron (MLP) with an alternative representation of the input vector. Estimating the quality of peaks with a Gaussian distribution could further improve the detection. Overall we could improve the root mean square error (RMSE) from 23.97 to 8.62.

Sascha Meudt | Markus Kächele | Günther Palm | Friedhelm Schwenker | Viktor Kessler

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