Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

Vahid Soleimani; Majid Mirmehdi; Dima Damen; James Dodd

doi:10.1109/ICIP.2018.8451202

Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

Vahid Soleimani, Majid Mirmehdi, Dima Damen, James Dodd

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (Scopus)

325 Downloads (Pure)

Abstract

We present a vision-based trunk-motion tolerant approach which estimates lung volume–time data remotely in forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests. After temporal modelling of trunk shape, generated using two opposing Kinects in a sequence, the chest-surface respiratory pattern is computed by performing principal component analysis on temporal geometrical features extracted from the chest and posterior shapes. We evaluate our method on a publicly available dataset of 35 subjects (300 sequences) and compare against the state-of-the-art. By filtering complex trunk motions, our proposed method calibrates the entire volume–time data using only the tidal volume scaling factor which reduces the state-of-the-art average normalised L2 error from 0.136 to 0.05.

Original language	English
Title of host publication	2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Subtitle of host publication	Proceedings of a meeting held 7-10 October 2018, Athens, Greece
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2077-2081
Number of pages	5
ISBN (Electronic)	9781479970612
ISBN (Print)	9781479970629
DOIs	https://doi.org/10.1109/ICIP.2018.8451202
Publication status	Published - Feb 2019

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Structured keywords

Digital Health
SPHERE

Keywords

trunk shape modelling , noncontact vision-based respiratory assessment , chest shape , spirometry

Access to Document

10.1109/ICIP.2018.8451202

Full-text PDF (accepted author manuscript)
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Accepted author manuscript, 2.03 MB

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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

Cite this

Soleimani, V., Mirmehdi, M., Damen, D., & Dodd, J. (2019). Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings of a meeting held 7-10 October 2018, Athens, Greece (pp. 2077-2081). [8451202] (Proceedings - International Conference on Image Processing, ICIP). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2018.8451202

Soleimani, Vahid ; Mirmehdi, Majid ; Damen, Dima et al. / Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment. 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings of a meeting held 7-10 October 2018, Athens, Greece. Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 2077-2081 (Proceedings - International Conference on Image Processing, ICIP).

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title = "Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment",

abstract = "We present a vision-based trunk-motion tolerant approach which estimates lung volume–time data remotely in forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests. After temporal modelling of trunk shape, generated using two opposing Kinects in a sequence, the chest-surface respiratory pattern is computed by performing principal component analysis on temporal geometrical features extracted from the chest and posterior shapes. We evaluate our method on a publicly available dataset of 35 subjects (300 sequences) and compare against the state-of-the-art. By filtering complex trunk motions, our proposed method calibrates the entire volume–time data using only the tidal volume scaling factor which reduces the state-of-the-art average normalised L2 error from 0.136 to 0.05.",

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author = "Vahid Soleimani and Majid Mirmehdi and Dima Damen and James Dodd",

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Soleimani, V, Mirmehdi, M , Damen, D & Dodd, J 2019, Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment. in 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings of a meeting held 7-10 October 2018, Athens, Greece., 8451202, Proceedings - International Conference on Image Processing, ICIP, Institute of Electrical and Electronics Engineers (IEEE), pp. 2077-2081. https://doi.org/10.1109/ICIP.2018.8451202

Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment. / Soleimani, Vahid; Mirmehdi, Majid ; Damen, Dima et al.
2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings of a meeting held 7-10 October 2018, Athens, Greece. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 2077-2081 8451202 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

AU - Soleimani, Vahid

AU - Mirmehdi, Majid

AU - Damen, Dima

AU - Dodd, James

PY - 2019/2

Y1 - 2019/2

N2 - We present a vision-based trunk-motion tolerant approach which estimates lung volume–time data remotely in forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests. After temporal modelling of trunk shape, generated using two opposing Kinects in a sequence, the chest-surface respiratory pattern is computed by performing principal component analysis on temporal geometrical features extracted from the chest and posterior shapes. We evaluate our method on a publicly available dataset of 35 subjects (300 sequences) and compare against the state-of-the-art. By filtering complex trunk motions, our proposed method calibrates the entire volume–time data using only the tidal volume scaling factor which reduces the state-of-the-art average normalised L2 error from 0.136 to 0.05.

AB - We present a vision-based trunk-motion tolerant approach which estimates lung volume–time data remotely in forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests. After temporal modelling of trunk shape, generated using two opposing Kinects in a sequence, the chest-surface respiratory pattern is computed by performing principal component analysis on temporal geometrical features extracted from the chest and posterior shapes. We evaluate our method on a publicly available dataset of 35 subjects (300 sequences) and compare against the state-of-the-art. By filtering complex trunk motions, our proposed method calibrates the entire volume–time data using only the tidal volume scaling factor which reduces the state-of-the-art average normalised L2 error from 0.136 to 0.05.

KW - trunk shape modelling , noncontact vision-based respiratory assessment , chest shape , spirometry

U2 - 10.1109/ICIP.2018.8451202

DO - 10.1109/ICIP.2018.8451202

M3 - Conference Contribution (Conference Proceeding)

SN - 9781479970629

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 2077

EP - 2081

BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Soleimani V, Mirmehdi M , Damen D , Dodd J. Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings of a meeting held 7-10 October 2018, Athens, Greece. Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 2077-2081. 8451202. (Proceedings - International Conference on Image Processing, ICIP). Epub 2018 Oct 7. doi: 10.1109/ICIP.2018.8451202

Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

Abstract

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Keywords

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Projects

SPHERE (EPSRC IRC)

Cite this