An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8594587/ |
| _version_ | 1818416084638760960 |
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| author | Chenglu Sun Wei Li Chen Chen Zeyu Wang Wei Chen |
| author_facet | Chenglu Sun Wei Li Chen Chen Zeyu Wang Wei Chen |
| author_sort | Chenglu Sun |
| collection | DOAJ |
| description | 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, “image skeleton” 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% for all data while utilizing polysomnography thorax effort signal as gold standard. Through the Bland–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. |
| first_indexed | 2024-12-14T11:45:16Z |
| format | Article |
| id | doaj.art-a86aef96632a4cc6bf355ef0eabb5e1d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:45:16Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a86aef96632a4cc6bf355ef0eabb5e1d2022-12-21T23:02:38ZengIEEEIEEE Access2169-35362019-01-0178300831510.1109/ACCESS.2018.28900828594587An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth ImagesChenglu Sun0https://orcid.org/0000-0002-9957-4973Wei Li1Chen Chen2https://orcid.org/0000-0001-7587-3314Zeyu Wang3Wei Chen4Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, ChinaIn 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, “image skeleton” 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% for all data while utilizing polysomnography thorax effort signal as gold standard. Through the Bland–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.https://ieeexplore.ieee.org/document/8594587/Unobtrusive monitoringrespiratory ratedepth imagingRealsense SR300respiratory region detecting algorithm |
| spellingShingle | Chenglu Sun Wei Li Chen Chen Zeyu Wang Wei Chen An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images IEEE Access Unobtrusive monitoring respiratory rate depth imaging Realsense SR300 respiratory region detecting algorithm |
| title | An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images |
| title_full | An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images |
| title_fullStr | An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images |
| title_full_unstemmed | An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images |
| title_short | An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images |
| title_sort | unobtrusive and non contact method for respiratory measurement with respiratory region detecting algorithm based on depth images |
| topic | Unobtrusive monitoring respiratory rate depth imaging Realsense SR300 respiratory region detecting algorithm |
| url | https://ieeexplore.ieee.org/document/8594587/ |
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