Photoplethysmographic signals obtained from a webcam are analyzed through a continuous wavelet transform to assess the instantaneous heart rate. The measurements are performed on human faces. Robust image and signal processing are introduced to overcome drawbacks induced by light and motion artifacts. In addition, the respiration signal is recovered using the heart rate series by respiratory sinus arrhythmia, the natural variation in heart rate driven by the respiration. The presented algorithms are implemented on a mid-range computer and the overall method works in real-time. The performance of the proposed heart and breathing rates assessment method was evaluated using approved contact probes on a set of 12 healthy subjects. Results show high degrees of correlation between physiological measurements even in the presence of motion. This paper provides a motion-tolerant method that remotely measures the instantaneous heart and breathing rates. These parameters are particularly used in telemedicine and affective computing, where the heart rate variability analysis can provide an index of the autonomic nervous system.

We introduce a new framework for detecting mental workload changes using video frames obtained from a low-cost webcam. Image processing in addition to a continuous wavelet transform filtering method were developed and applied to remove major artifacts and trends on raw webcam photoplethysmographic signals. The measurements are performed on human faces. To induce stress, we have employed a computerized and interactive Stroop color word test on a set composed by twelve participants. The electrodermal activity of the participants was recorded and compared to the mental workload curve assessed by merging two parameters derived from the pulse rate variability and photoplethysmographic amplitude fluctuations, which reflect peripheral vasoconstriction changes. The results exhibit strong correlation between the two measurement techniques. This study offers further support for the applicability of mental workload detection by remote and low-cost means, providing an alternative to conventional contact techniques.

In the present paper, we introduce a new framework for detecting workload changes using video frames obtained from a low-cost webcam. The measurements are performed on human faces and the proposed algorithms were developed to be motion-tolerant. An interactive Stroop color word test is employed to induce stress on a set of twelve participants. We record the skin conductance and compare these responses to the stress curve assessed by a webcam-derived heart rate variability analysis. The results offer further support for the applicability of stress detection by remote and low-cost means, providing an alternative to conventional contact techniques.

We propose, in this study, an original method that was developed to remotely measure the instantaneous pulse rate using photoplethysmographic signals that were recorded from a low-cost webcam. The method is based on a prior selection of pixels of interest using a custom segmentation that used the face lightness distribution to define different sub-regions. The most relevant subregions are automatically selected and combined by evaluating their respective signal to noise ratio. Performances of the proposed technique were evaluated using an approved contact sensor on a set of 7 healthy subjects. Different experiments while reading, with motion or while performing common tasks on a computer were conducted in the laboratory. The proposed segmentation technique was compared with other benchmark methods that were already introduced in the scientific literature. The results exhibit high degrees of correlation and low pulse rate absolute errors, demonstrating that the segmentation we propose in this study outperform available region-of-interest selection methods.

Recent technological advances in the field of sensors, signal processing and image processing favor the development of new techniques for vital parameters monitoring such as imaging photoplethysmography (iPPG). iPPG is a simple and noninvasive measurement technique. It has been employed to remotely estimate heart and respiratory rates, oxygen saturation and blood pressure through the measurement of blood volume pulse using a camera. In the recent decades, researchers used the morphology of contact photoplethysmographic (cPPG) signal for the assessment of arterial stiffness, blood pressure, arteriosclerosis, cardiac output, and vascular aging. We propose to study, in this article, the similarities between iPPG and cPPG waveform features that are associated to cardiovascular diseases. A fast camera and contact probes were respectively employed to record iPPG and cPPG signals. Their waveform features such as time, areas, amplitude and second derivative features were then extracted and analyzed. Results show a high correlation between the two measurement techniques. This research opens several perspectives in the remote assessment of blood pressure and arterial stiffness, and therefore for non-contact diagnosis of several cardiovascular diseases.