Article Text

P83 Remote pulmonary function testing – computer gaming in the respiratory world
  1. C Sharp1,
  2. V Soleimani2,
  3. S Hannuna2,
  4. M Camplani2,
  5. D Damen2,
  6. J Viner3,
  7. M Mirmehdi2,
  8. J Dodd1
  1. 1Academic Respiratory Unit, University of Bristol, Bristol, UK
  2. 2Visual Information Laboratory, Faculty of Engineering, University of Bristol, Bristol, UK
  3. 3Respiratory Physiology, North Bristol NHS Trust, Bristol, UK


Introduction Lung function testing by spirometry has remained unchanged for over 50 years, despite limitations including patient technique, discomfort, cost and training. Non-invasive, remote lung volume measurement is an alternative approach. This has been explored in the past using structured light, accelerometers and impedance pneumography, but these have often been costly and of limited validity.

We use a novel approach to remote assessment (~2 metres) using a 3D time-of-flight depth camera – similar to those found in many home gaming consoles. This pilot developmental data was generated from patients in a clinical setting.

Methods Patients were recruited from a general respiratory physiology laboratory. Spirometry was performed according to ATS/ERS standards using an unmodified pneumotachograph (nSpire Health, Longmont, CO, USA). A Kinect V2 time-of-flight depth sensor (Microsoft, Redmond, WA, USA) was used to reconstruct 3D models of the subject’s thorax to estimate volume-time and flow-time curves for both Forced and Slow Vital Capacity and their associated measurements (Figure 1, technical details in1).

Abstract P83 Figure 1

(A) Reconstructed chest surface and volume estimation from reference plane. (B) Volume-time curves (FVC and SVC) comparing spirometer values and Kinect calculation

These results were correlated with simultaneous recordings from the pneumotachograph, and error values calculated to assess the accuracy of the technique.

Results Data were available from 53 patients, with 40 having usable data. Mean age 62.8 yrs (SD 16.2), BMI of 26.8 (SD 5.5). 41.5% male. 54.7% of patients had obstructive lung diseases, and 28.4% fibrotic lung disease. Mean FVC was 91.3% predicted (SD 26.4%), Mean FEV1 83.1% (SD 28.9%).

The model estimates were highly correlated with spirometric values for FVC (λ = 0.999), FEV1 (λ = 0.947), VC (λ = 0.999), IC (λ = 0.997) and TV (λ = 0.962).

Univariate analysis demonstrated no patient characteristics predictive of discrepancy from spirometric values for FVC or VC.

Conclusions We describe a pilot data from the initial development of a new technique for non-invasively assessing lung volume and pulmonary function measurements. It correlates to within 30 ml for FVC and 10 ml for VC. This has a wide range of potential applications, including screening, home monitoring of respiratory disease, assessment of lung function in those unable to complete pneumotachygraphy and gating controls for radiological imaging techniques.

Reference 1 Soleimani V, Mirmehdi M, Damen D, et al. Remote pulmonary function testing using a depth sensor. Biocas 2015

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