Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the Vision for Vitals dataset was introduced. It is a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. It contains more than 150+ subjects with over 1300+ videos along with ground truth heart rate and respiration rate annotations. It also includes blood pressure waveform signals as part of its physiological data.
The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes: * Hand/head/chair movements. * Heart rate. * Muscle activity. * Gaze movement on the monitor. * Galvanic skin response(GSR). * Electroencephalography (EEG). * Mouse and keyboard activity. * Facial skin temperature. * Environmental data. The data were collected for one team of 5 people simultaneously. In-game logs and meta information for each match are also provided for each match. Source: https://github.com/smerdov/eSports_Sensors_Dataset