# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 4 Datasets  
To compare against prior work and study the properties of the proposed method, we evaluate on a comprehensive collection of datasets. Figure 3 provides summarized details of existing datasets used in this paper, while the self-recorded ones are listed below. Some example frames from these self-recorded datasets are shown in Figure 4.  
StableSet rPPG datasetTo make a proof-of-concept test of our proposed rPPG method, the StableSet rPPG dataset was collected. This dataset contains recordings of participants while they were shown short video clip stimuli on a screen facing them. During the recording of this dataset, the participating subjects' head movements were physically stabilized using a chin rest with the intention of minimizing motion induced noise in rPPG measurements. A total of 24 participants were included in this dataset, aged between 18 and 30 years (with a mean of 21.5 years), and having a male: female gender ratio of \(9:15\). Ground Truth was collected in the form of ECG (via a Mobi8 device) at a sampling rate of 1 KHz, and the videos were recorded using a front-facing HD camcorder (JVC GZ-VX815) with a resolution of 1920\(\times\)1080 pixels at a framerate of 25 fps. Camera settings like brightness, aperture, backlight compensation and white balance were set to manual on the camcorder to keep filming conditions ideal and constant. Prior informed consent was obtained from all participants. However, due to privacy restrictions within the consent, the videos of this dataset are not made available publicly. Figure 4: Example images from newly introduced datasets: (left to right) VicarPPG 2, EatingSet, and StableSet. The example from VicarPPG 2 shows subject suddenly turning their head. The EatingSet image shows subject taking a long sip resulting in face occlusion. The subjects in StableSet were physically stabilized using the shown chin rest (face removed for privacy reasons). Figure 3: A list of the previously available rPPG datasets used in the this paper, along with key details [6,11,12,21,33,36,38,39,45,46]. VicarPPG-2 dataset. Specifically aimed at evaluating rPPG algorithms at estimating heart rate and short-term heart rate variability (which requires a minimum observation of 5 min [10]), we recorded the VicarPPG-2 Dataset. In total, 10 subjects participated in the data collection. The male to female ratio was 7:3 with an average age of 29 \(\pm\) 5 years, and skin types ranging on a Fitzpatrick scale [47] from II to IV. Participants were asked to sit in front of a computer screen (\(\sim\)1 m distance) on which the instructions were shown, a webcam was mounted on top of the screen and an LED ring lamp was mounted behind the camera. Screen brightness was reduced as far as possible to minimize the influence of screen light on the face. All videos were recorded using a Logitech Brio webcam at a fixed framerate of 60 fps using an H.264 compliant encoder (Microsoft Media Foundation), and stored in mp4 containers. The recording location was illuminated by natural ambient light in addition to a LED ring lamp (Falcon Eyes DVR-300 DVC) to prevent strong shadows and influences of large changes in natural light. The ground truth signals were recorded in the form of synchronized ECG signals at 250 Hz sampling frequency obtained via an Arduino based ECG board (AD8232), and synchronized PPG signals at 60 Hz obtained via a pulse oximeter device (CMS50E) attached to the left index finger of the participant. The following four scenarios/conditions were recorded for each participant:  
**(i)**: Baseline: Participants sitting naturally while watching a relaxing video or reading an article on screen. **(ii)**: Movement: Participants performing four different types of pre-planned angular body/head movements: turning head side-to-side (shaking), moving head up and down (nodding), a combination of head shaking and nodding (round), moving eyes while keeping head still, and naturally bobbing their heads while listening to music (dance).