Projects per year
Method: Following spatial and temporal calibration of two opposing RGB-D sensors, a dynamic 3-D model of the subject performing PFT is reconstructed and used to decouple trunk movements from respiratory motions. Depth-based volume–time data is then retrieved, calibrated and used to compute 11 clinical PFT measures for forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests.
Results: A dataset of 35 subjects (298 sequences) was collected and used to evaluate the proposed dPPG method by comparing depth-based PFT measures to the measures provided by a spirometer. Other comparative experiments between the dPPG and the single Kinect approach, such as Bland-Altman analysis, similarity measures performance, intra-subject error analysis, and statistical analysis of tidal volume and main effort scaling factors, all show the superior accuracy of the dPPG approach.
Conclusion: We introduce a depth-based whole body photoplethysmography approach which reduces motion artifacts in depth-based volume–time data and highly improves the accuracy of depth-based computed measures.
Significance: The proposed dPPG method remarkably drops the L2 error mean and standard deviation of FEF50% , FEF75% , FEF25-75% , IC, and ERV measures by half, compared to the single Kinect approach. These significant improvements establish the potential for unconstrained remote respiratory monitoring and diagnosis.
|Number of pages||11|
|Journal||IEEE Transactions on Biomedical Engineering|
|Early online date||11 Dec 2017|
|Publication status||Published - Jun 2018|
- Digital Health
- 3-D body reconstruction
- depth-based photoplethysmography (dPPG)
- forced vital capacity (FVC)
- lung function assessment
- motion artifacts reduction
- motion decoupling
- pulmonary function testing
- slow vital capacity (SVC)
- Digital Health
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- 1 Finished
SPHERE (EPSRC IRC)
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
A Dataset for Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing
Soleimani, V. (Creator), Mirmehdi, M. (Creator), Damen, D. (Creator), Camplani, M. (Contributor), Hannuna, S. L. (Contributor), Sharp, C. (Contributor), Dodd, J. (Contributor) & Mirmehdi, M. (Data Manager), University of Bristol, 13 Feb 2018
DOI: 10.5523/bris.1tqzx39mzkw832msuvy3obktqi, http://data.bris.ac.uk/data/dataset/1tqzx39mzkw832msuvy3obktqi
Professor Majid Mirmehdi
- Department of Computer Science - Professor of Computer Vision/Engineering Faculty Education Director
- Visual Information Laboratory
- Bristol Vision Institute
Person: Academic , Member