Depth-Based Measurement of Respiratory Volumes: A Review
Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9680 |
| _version_ | 1797455397261410304 |
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| author | Felix Wichum Christian Wiede Karsten Seidl |
| author_facet | Felix Wichum Christian Wiede Karsten Seidl |
| author_sort | Felix Wichum |
| collection | DOAJ |
| description | Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation. |
| first_indexed | 2024-03-09T15:52:49Z |
| format | Article |
| id | doaj.art-b5ba542574b04616a084ed53623a1d83 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:49Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b5ba542574b04616a084ed53623a1d832023-11-24T17:53:33ZengMDPI AGSensors1424-82202022-12-012224968010.3390/s22249680Depth-Based Measurement of Respiratory Volumes: A ReviewFelix Wichum0Christian Wiede1Karsten Seidl2Fraunhofer IMS, 47057 Duisburg, GermanyFraunhofer IMS, 47057 Duisburg, GermanyFraunhofer IMS, 47057 Duisburg, GermanyDepth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.https://www.mdpi.com/1424-8220/22/24/9680tidal volumevital capacitycontactless measurementkinectstructured lighttime-of-flight |
| spellingShingle | Felix Wichum Christian Wiede Karsten Seidl Depth-Based Measurement of Respiratory Volumes: A Review Sensors tidal volume vital capacity contactless measurement kinect structured light time-of-flight |
| title | Depth-Based Measurement of Respiratory Volumes: A Review |
| title_full | Depth-Based Measurement of Respiratory Volumes: A Review |
| title_fullStr | Depth-Based Measurement of Respiratory Volumes: A Review |
| title_full_unstemmed | Depth-Based Measurement of Respiratory Volumes: A Review |
| title_short | Depth-Based Measurement of Respiratory Volumes: A Review |
| title_sort | depth based measurement of respiratory volumes a review |
| topic | tidal volume vital capacity contactless measurement kinect structured light time-of-flight |
| url | https://www.mdpi.com/1424-8220/22/24/9680 |
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