Abstract
In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user’s heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).
Highlights
Photopletysmography (PPG) is a non-invasive, low-cost optical technique used to detect volumetric changes in blood in the peripheral circulation
We found that method for greenness identification [36] utilizing the excess green image component (ExG), amplify the pulse signal and it is faster to compute than the independent component analysis (ICA)
Different kinds of metrics were proposed in other articles for evaluating the accuracy of heart rate (HR) measurement methods
Summary
Photopletysmography (PPG) is a non-invasive, low-cost optical technique used to detect volumetric changes in blood in the peripheral circulation. One of the first approaches was proposed by Verkruysse et al [12], who showed that plethysmographic signals can be remotely measured from a human face in normal ambient light using a simple digital, consumer level photo camera The advantages of this approach, compared to standard photopletysmography (PPG) techniques, are that it does not require uncomfortable wearable accessories and allows easy adaptation to different requirements in various applications, such as: monitoring the driver’s vital signs in the automotive industry [13], optimization of training in sport [14] and emotional communication in the field of human-machine interaction [15]
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