# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 4 Datasets
**(iii)**: Stress: Participants playing a stress-inducing Stroop effect [48] based game. **(iv)**: Post-workout: Participants sitting unrestrained after performing fatigue-inducing physical workouts to induce higher heart rates. Each condition was recorded for a duration of 5 min to allow for the computation of short-term heart rate variabilty, a total of 200 min of video were collected. Out of 400 collected ground truth files, two had to be removed from the dataset due to excessive finger movement in the PPG device and gradual detachment of the ECG ground electrode, leading to unusable signals. This dataset stands out as it was explicitly collected for RPPG purposes featuring 5-min long 60 fps camera recordings under various physical/pysiological conditions, with simultaneous ECG and PPG ground truth recordings. Informed consent was obtained from all participating subjects. This dataset is available for research purpose, and can be requested via e-mail to the corresponding author(s). EatingSet rPPG dataset. This is a self-recorded video dataset comprising of 20 subjects, with a male: female ratio of 14 : 6. The average age in the dataset was 32 \(\pm\) 8.6 years, and the subjects had skin types ranging on the Fitzpatrick scale [47] from II to IV. The recording setup and conditions were similar to those of VicarPPG 2. Participants were sitting at 1 m distance to a screen on which instructions were shown, while being illuminated by an LED ring lamp. All videos were recorded using a Logitech C920 webcam at 30 fps in uncompressed YUYV422 pixel format. PPG signals were collected as ground truth, at a frequency of 60 Hz, via a pulse oximeter device (CMS50E) attached to the left index finger of the participant. During recording participants were asked to consume four types of food items with varying consistency. These include a sign of water (drink), a cookie (crumbly), a marshallow (chewy) and multiple almonds (hard). Informed consent was obtained from all participants. However, due to privacy restrictions, the videos of this dataset are not made available publicly. This unique dataset contains a variety of natural, non-rhythmic deformations and facial occlusions while eating as they might occur in real-world situations, and serves as a challenging testbed for rPPG evaluation. CleanerPPG ground truth dataset. Finally, we introduce a meta-dataset which contains cleaned ground truth signals of already existing datasets. All peaks/beats detected from ground truth signals(ECG, PPG) of all datasets used in this paper were hand-verified and corrected by human expert annotators, in order to achieve a more accurate and standardized evaluation. To do this, candidate peaks were first obtained using a gradient-based signal peak detector [49]. Following this, a human verification step was performed wherein an expert matched, verified and corrected every candidate peak by observing the shape of the raw PPG/ECG waveform and the inter-beat intervals. A specialized Python tool was developed for this step, which allow annotation of single peaks as well as zooming and an overview of the resulting RR intervals. Peaks were annotated at the crest of the PPG signal or at the highest point of the R peak in the ECG signal. Ectopic beats (genuine extra heart beats that can occur between two regular beats) in the ECG signal were included for the sake of completion. Parts of the signal that were too noisy due to movement of electrodes or finger to make out clear peaks were left blank. These steps led to the removal of false positive and negative peak detections caused by artefacts in the signal, resulting in a collection of noise-free ground truth heart beat annotations for an exhaustive set of publicly available rPPG datasets. Annotators spent an average of \(\sim\)30 s per minute of signal duration, results in a total of 36 person-hours of annotation work for cleaning 75 h of ground truth data. These annotations can be used to perform a more accurate evaluation of rPPG methods, especially for noise-sensitive measures like HRV.