Depth-based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Vahid Soleimani, Majid Mirmehdi, Dima Damen, Massimo Camplani, Sion Hannuna, Charles Sharp, James Dodd

Research output: Contribution to journalArticle (Academic Journal)

5 Citations (Scopus)
238 Downloads (Pure)

Abstract

Objective: We  propose a novel depth-based Photoplethysmography (dPPG) approach to reduce motion artifacts in respiratory volume–time data and improve the accuracy of remote pulmonary function testing (PFT) measures.

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.
Original languageEnglish
Article number8186188
Pages (from-to)1421-1431
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume65
Issue number6
Early online date11 Dec 2017
DOIs
Publication statusPublished - Jun 2018

Structured keywords

  • Digital Health

Keywords

  • 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)
  • spirometry
  • Digital Health

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  • Projects

    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., Aldamen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.

    1/10/1330/09/18

    Project: Research, Parent

    Datasets

    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

    Dataset

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