The use of time‐of‐flight camera to assess respiratory rates and thoracoabdominal depths in patients with chronic respiratory disease

Author:

Van Hove Olivier1ORCID,Andrianopoulos Vasileios2ORCID,Dabach Ali3,Debeir Olivier3ORCID,Van Muylem Alain1,Leduc Dimitri14,Legrand Alexandre5,Ercek Rudy3,Feipel Véronique6,Bonnechère Bruno78ORCID

Affiliation:

1. Department of Pneumology Erasme Hospital Brussels Belgium

2. Institute for Pulmonary Rehabilitation Research Schoen Klinik Berchtesgadener Land Schoenau am Koenigssee Germany

3. LISA ‐ Laboratory of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium

4. Laboratory of Cardiorespiratory Physiology Université Libre de Bruxelles Brussels Belgium

5. Department of Respiratory Physiology, Pathophysiology and Rehabilitation Research Institute for Health Sciences and Technology, University of Mons Mons Belgium

6. Laboratory of Functional Anatomy Université Libre de Bruxelles Brussels Belgium

7. REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences Hasselt University Diepenbeek Belgium

8. Technology‐Supported and Data‐Driven Rehabilitation, Data Sciences Institute Hasselt University Diepenbeek Belgium

Abstract

AbstractIntroductionOver the last 5 years, the analysis of respiratory patterns presents a growing usage in clinical and research purposes, but there is still currently a lack of easy‐to‐use and affordable devices to perform such kind of evaluation.ObjectivesThe aim of this study is to validate a new specifically developed method, based on Kinect sensor, to assess respiratory patterns against spirometry under various conditions.MethodsOne hundred and one participants took parts in one of the three validations studies. Twenty‐five chronic respiratory disease patients (14 with chronic obstructive pulmonary disease (COPD) [65 ± 10 years old, FEV1 = 37 (15% predicted value), VC = 62 (20% predicted value)], and 11 with lung fibrosis (LF) [64 ± 14 years old, FEV1 = 55 (19% predicted value), VC = 62 (20% predicted value)]) and 76 healthy controls (HC) were recruited. The correlations between the signal of the Kinect (depth and respiratory rate) and the spirometer (tidal volume and respiratory rate) were computed in part 1. We then included 66 HC to test the ability of the system to detect modifications of respiratory patterns induced by various conditions known to modify respiratory pattern (cognitive load, inspiratory load and combination) in parts 2 and 3.ResultsThere is a strong correlation between the depth recorded by the Kinect and the tidal volume recorded by the spirometer: r = 0.973 for COPD patients, r = 0.989 for LF patients and r = 0.984 for HC. The Kinect is able to detect changes in breathing patterns induced by different respiratory disturbance conditions, gender and oral task.ConclusionsMeasurements performed with the Kinect sensors are highly correlated with the spirometer in HC and patients with COPD and LF. Kinect is also able to assess respiratory patterns under various loads and disturbances. This method is affordable, easy to use, fully automated and could be used in the current clinical context.Respiratory patterns are important to assess in daily clinics. However, there is currently no affordable and easy‐to‐use tool to evaluate these parameters in clinics. We validated a new system to assess respiratory patterns using the Kinect sensor in patients with chronic respiratory diseases.

Publisher

Wiley

Subject

Genetics (clinical),Pulmonary and Respiratory Medicine,Immunology and Allergy

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