An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

• Main Authors: Chenglu Sun, Wei Li, Chen Chen, Zeyu Wang, Wei Chen
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Unobtrusive monitoring; respiratory rate; depth imaging; Realsense SR300; respiratory region detecting algorithm
• Online Access: https://ieeexplore.ieee.org/document/8594587/

In order to obtain the respiratory condition unobtrusively and comfortably, a non-contact method based on the commercial depth camera Realsense SR300 was proposed to extract respiratory information from depth data. In this paper, a respiratory region detecting algorithm which is mainly based on the morphological method was proposed to obtain the region of interest (ROI) with the depth images. The proposed algorithm contains four steps: body edge extraction, noise reduction, &#x201C;image skeleton&#x201D; extraction, and respiratory region estimation. As a result, the respiratory waveform can be derived from the depth data in the ROI. For validation, experiments were carried out to verify the feasibility of obtaining the respiratory information with this approach. In consideration of different application scenarios, 20 kinds of conditions were designed and applied for the experiments. The respiratory rate extracted from the depth waveform can be calculated, and the accuracy achieved was 95.20&#x0025; for all data while utilizing polysomnography thorax effort signal as gold standard. Through the Bland&#x2013;Altman analysis, it represented that the proposed system had a good agreement (<inline-formula> <tex-math notation="LaTeX">$r^{2} = 0.88$ </tex-math></inline-formula>) with the gold standard. In addition, the performances of the system in the 20 different conditions were analyzed by statistics, and the results showed that the system has good adaptability and robustness for different conditions. In conclusion, the proposed algorithm can fit different scenarios, and this paper provides a novel option for extracting the physiological information with depth data.