A Region of Interest Selection for Vision based Respiratory Rate Measurement in Sleeping Scenario

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Wu, Bing-Fei, Lin, Chun-Han, Huang, Po-Wei, Chen, Kuan-Hung, He, Da-Hong
• Format: Article
• Language: English
• Subjects: Algorithms; Estimation; Face; Face detection; Feature extraction; Frames per second; Image sequences; Optical imaging; region of interest; Respiratory rate; Respiratory rate monitoring; Signal processing; Signal to noise ratio; Sleep apnea; Vision
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012676

Vision-based respiratory measurement can remotely measure respiratory information without affecting the sleep quality of the user. However, several scenarios during sleeping, such as when blankets cover areas of the face or body, can be a challenge when estimate respiratory rate (RR) due to the uncertainty of the region of interest (ROI). In this study, we first investigate the metrics and physical meanings of ROI selection from the perspectives of array signal processing and the concept of linear combining. Then, we propose an ROI detection algorithm based on both temporal and spatial consistency (TSC), which aims to extract the representative characteristics of respiration with fewer computational resources. Furthermore, a sleeping database, containing more than 50 hours of data, was built to investigate the performance of benchmarked ROI methods during long-term sleeping. The experimental results demonstrate that TSC attains the highest signal-to-noise ratio (SNR) at 20.6 dB and a relatively low elapsed time of 100.2 frames per second (fps). For the RR estimation accuracy, TSC reduces the mean absolute error from 1.65 breaths per minute (bpm) to 0.97 bpm.