Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

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

3 Citations (Scopus)
325 Downloads (Pure)


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 languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Subtitle of host publicationProceedings of a meeting held 7-10 October 2018, Athens, Greece
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781479970612
ISBN (Print)9781479970629
Publication statusPublished - Feb 2019

Publication series

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

Structured keywords

  • Digital Health


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


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