Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony
Background and objective: This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images. Methods: The proposed method employs singular value decomposition (SVD) to extrac...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000073 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849708031862898688 |
|---|---|
| author | Masaru Mitsuya Hiroki Nishine Hiroshi Handa Masamichi Mineshita Masaki Kurosawa Tetsuo Kirimoto Shohei Sato Takemi Matsui Guanghao Sun |
| author_facet | Masaru Mitsuya Hiroki Nishine Hiroshi Handa Masamichi Mineshita Masaki Kurosawa Tetsuo Kirimoto Shohei Sato Takemi Matsui Guanghao Sun |
| author_sort | Masaru Mitsuya |
| collection | DOAJ |
| description | Background and objective: This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images. Methods: The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony. Results: A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes. Conclusions: The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology. |
| format | Article |
| id | doaj-art-c92142ec94314204bc95ab86b2f363dd |
| institution | DOAJ |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-c92142ec94314204bc95ab86b2f363dd2025-08-20T03:15:47ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015310161910.1016/j.imu.2025.101619Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchronyMasaru Mitsuya0Hiroki Nishine1Hiroshi Handa2Masamichi Mineshita3Masaki Kurosawa4Tetsuo Kirimoto5Shohei Sato6Takemi Matsui7Guanghao Sun8Department of Biomedical Engineering, Graduate School of Medicine, Science and Technology, Shinshu University, JapanDivision of Respiratory Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, JapanDivision of Respiratory Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, JapanDivision of Respiratory Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, JapanGraduate School of Informatics and Engineering, The University of Electro-Communications, JapanGraduate School of Informatics and Engineering, The University of Electro-Communications, JapanGraduate School of System Design, Tokyo Metropolitan University, JapanGraduate School of System Design, Tokyo Metropolitan University, JapanGraduate School of Informatics and Engineering, The University of Electro-Communications, Japan; Corresponding author. 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan.Background and objective: This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images. Methods: The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony. Results: A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes. Conclusions: The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology.http://www.sciencedirect.com/science/article/pii/S2352914825000073Point-of-careSpatiotemporal analysisSingular value decompositionNoninvasive |
| spellingShingle | Masaru Mitsuya Hiroki Nishine Hiroshi Handa Masamichi Mineshita Masaki Kurosawa Tetsuo Kirimoto Shohei Sato Takemi Matsui Guanghao Sun Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony Informatics in Medicine Unlocked Point-of-care Spatiotemporal analysis Singular value decomposition Noninvasive |
| title | Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| title_full | Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| title_fullStr | Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| title_full_unstemmed | Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| title_short | Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| title_sort | spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony |
| topic | Point-of-care Spatiotemporal analysis Singular value decomposition Noninvasive |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000073 |
| work_keys_str_mv | AT masarumitsuya spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT hirokinishine spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT hiroshihanda spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT masamichimineshita spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT masakikurosawa spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT tetsuokirimoto spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT shoheisato spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT takemimatsui spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony AT guanghaosun spatiotemporalchestwallmovementanalysisusingdepthsensorimagingfordetectingrespiratoryasynchrony |