Improved Touchless Respiratory Rate Sensing
This work addresses the need for non-invasive respiratory monitoring, but it is incremental as it builds on existing pixel intensity changes methods.
The paper tackled the problem of remote respiratory rate measurement by proposing a new method for 1D profile creation and motion signals grouping, which improved algorithm performance, achieving mean absolute errors of 0.7 BPM, 0.6 BPM, and 1.4 BPM on three datasets.
Recently, remote respiratory rate measurement techniques gained much attention as they were developed to overcome the limitations of device-based classical methods and manual counting. Many approaches for RR extraction from the video stream of the visible light camera were proposed, including the pixel intensity changes method. In this paper, we propose a new method for 1D profile creation for pixel intensity changes-based method, which significantly increases the algorithm's performance. Additional accuracy gain is obtained via a new method of motion signals grouping presented in this work. We introduce several changes to the standard pipeline, which enables real-time continuous RR monitoring and allows applications in the human-computer interaction systems. Evaluation results on two internal and one public datasets showed 0.7 BPM, 0.6 BPM, and 1.4 BPM MAE, respectively.