Lightweight Video-Based Respiration Rate Detection Algorithm: An Application Case on Intensive Care

Abstract

The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings, and various researchers are currently working on this technology. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. For these two challenges, two optimization points are proposed in this work: 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers that are not related to respiratory movements. The performance of the proposed algorithm is validated by the Large-scale Bedside Respiration Dataset for Intensive Care (LBRD-IC), which is established using the actual surveillance videos acquired from ICU wards. The validity of the above two optimization points is verified by the ablation experiments. The influential analysis of computation time and video resolution on the performance of the proposed algorithm demonstrates that the proposed algorithm can be deployed to various application terminals to monitor the respiration rate of living organisms in real-time and with high accuracy. In addition, field measurements in the ICU ward have shown that our algorithm can measure respiratory signals of the single patient and multiple patients when only one surveillance camera is present. The dataset and the code are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShawnTan86/LBRD-IC-Dataset</uri> .