Evaluating and improving the robustness of a video-based PR/RR monitoring system for a clinical environment

With the advancement and use of low cost cameras, video-based remote vital-monitoring technologies have drawn more and more attention. However, many of the previously proposed techniques rely on the assumption that the video is acquired under well-controlled conditions. There has been no study reported around how various acquisition factors and/or noise will affect the estimation accuracy. In this paper, we describe a systematic approach in understanding the challenges of extending the technology beyond a well-controlled environment and share the solution paths in making the technology robust in one specific application. The systematic approach involves first understanding the data analysis process, identifying key factors that affect the algorithms and then estimating the bounds of the factors and metrics that explain the variations in these vitals. As part of the solution paths, our paper goes into the details of how the algorithms were improved to address the findings of the current state. The refinement in the algorithms includes getting stronger signals in less controlled setting.