Portable computer vision-based cardiac estimation as a teaching aid

The prevalence of pervasive computing has made computing platforms more portable and has introduced an array of sensors that are useful for many applications. The potential of these sensors are finally being realized in many fields. One area that is especially benefiting from these sensors is the field of the biometrics. However, we are still grappling with user acceptance issues such as intrusiveness and hygiene problems, which stifle the uptake of the technology. This study investigates the potential of using one of the more common sensors, a portable device camera, and using it to assist educators. By implementing a system that captures heart rate in a novel manner, a basic affective biometric system is formed that requires no contact and is portable. The system segments relevant areas that highlight blood flow in the face, extrapolates heart rate variability through color space changes. By analyzing the extent of color change, a cardiac waveform can be formed with a QRS complex derivative, which can be used for the task of sentiment classification. The sentiment derived can then be used by an educator to inform them of any potential uncertainty during their teaching in real-time. Abrupt changes in sentiment can then be addressed during the class, thereby improving the potential uptake of concepts taught in a classroom. The study validates that it is possible to derive heart rate variability using a camera, it also shows that using this heart rate for basic sentiment classification is feasible using a portable device even with limited resources and it warrants more attention.