Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars
This work addresses a bottleneck in health monitoring for broader applications by enhancing machine vision approaches, though it is incremental as it builds on existing methods with synthetic data.
The paper tackled the problem of poor generalization in non-contact vital sign measurement due to limited annotated video datasets by using synthetic avatars to generate diverse samples, resulting in state-of-the-art performance on three large benchmarks and improved robustness to skin type and motion.
Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.