Measuring cognitive load and stress is crucial for ubiquitous human computer interaction applications to dynamically understand and respond to the mental status of users, such as in smart healthcare, smart driving, and robotics. Various quantitative methods have been employed for this purpose, such as physiological and behavioral methods. However, the sensitivity, reliability, and usability are not satisfactory in many of the current methods, so they are not ideal for ubiquitous applications. In this study, we employed a reflected photoplethysmogram-based stress-induced vascular response index, i.e., the reflected sVRI (sVRI-r), to non-invasively measure the cognitive load and stress. This method has high usability as well as good sensitivity and reliability compared with the previously proposed transmitted sVRI (sVRI-t). We developed the basic methodology and detailed algorithm framework to validate the sVRI-r measurements, and it was implemented by employing two light sources, i.e., infrared light and green light. Compared with the simultaneously recorded blood pressure, heart rate variation, and sVRI-t, our findings demonstrated the greater potential of the sVRI-r for use as a sensitive, reliable, and usable parameter, as well as suggesting its potential integration with ubiquitous touch interactions for dynamic cognition and stress-sensing scenarios.