SCAMPS: Synthetics for Camera Measurement of Physiological SignalsDownload PDF

Published: 17 Sept 2022, Last Modified: 22 Oct 2023NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Synthetics, Dataset, Health, Physiology, Computer Vision
TL;DR: SCAMPS is a dataset of high-fidelity synthetics containing 2,800 videos (1.68M frames) of avatars with aligned cardiac and respiratory signals and facial action intensities.
Abstract: The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs is very attractive. However, diverse data representing a range of environments, body motions, illumination conditions and physiological states is laborious, time consuming and expensive to obtain. Synthetic data have proven a valuable tool in several areas of machine learning, yet are not widely available for camera measurement of physiological states. Synthetic data offer "perfect" labels (e.g., without noise and with precise synchronization), labels that may not be possible to obtain otherwise (e.g., precise pixel level segmentation maps) and provide a high degree of control over variation and diversity in the dataset. We present SCAMPS, a dataset of synthetics containing 2,800 videos (1.68M frames) with aligned cardiac and respiratory signals and facial action intensities. The RGB frames are provided alongside segmentation maps and precise descriptive statistics about the underlying waveforms, including inter-beat interval, heart rate variability, and pulse arrival time. Finally, we present baseline results training on these synthetic data and testing on real-world datasets to illustrate generalizability.
Supplementary Material: pdf
URL: https://github.com/danmcduff/scampsdataset
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