Toward Respiratory Assessment Using Depth Measurements from a Time-of-Flight Sensor

Abstract

Introduction: There is increasing interest in technologies that may enable remote monitoring of respiratory disease. Traditional methods for assessing respiratory function such as spirometry can be expensive and require specialist training to perform and interpret. Remote, non-contact tracking of chest wall movement has been explored in the past using structured light, accelerometers and impedance pneumography, but these have often been costly and clinical utility remains to be defined. We present data from a 3-Dimensional time-of-flight camera (found in gaming consoles) used to estimate chest volume during routine spirometry maneuvres.Methods: Patients were recruited from a general respiratory physiology laboratory. Spirometry was performed according to international standards using an unmodified spirometer. A Microsoft Kinect V2 time-of-flight depth sensor was used to reconstruct 3-dimensional models of the subject's thorax to estimate volume-time and flow-time curves following the introduction of a scaling factor to transform measurements to volume estimates. The Bland-Altman method was used to assess agreement of model estimation with simultaneous recordings from the spirometer. Patient characteristics were used to assess predictors of error using regression analysis and to further explore the scaling factors.Results: The chest volume change estimated by the Kinect camera during spirometry tracked respiratory rate accurately and estimated forced vital capacity (FVC) and vital capacity to within ± <1%. Forced expiratory volume estimation did not demonstrate acceptable limits of agreement, with 61.9% of readings showing >150 ml difference. Linear regression including age, gender, height, weight, and pack years of smoking explained 37.0% of the variance in the scaling factor for volume estimation. This technique had a positive predictive value of 0.833 to detect obstructive spirometry.Conclusion: These data illustrate the potential of 3D time-of-flight cameras to remotely monitor respiratory rate. This is not a replacement for conventional spirometry and needs further refinement. Further algorithms are being developed to allow its independence from spirometry. Benefits include simplicity of set-up, no specialist training, and cost. This technique warrants further refinement and validation in larger cohorts.

Novel wearable and contactless monitoring devices to identify deteriorating patients in the clinical setting: a systematic review protocol

“…Novel vital sign monitoring devices can be classified into either the wearable/contact or contactless space, with the former typically disposable sensors applied via adhesive [24], contained in a smaller device in direct contact with the body [25,26] or in near or direct proximity to the patient via mattress or bedsheet using ballistography [27] or electrical bioimpedence [28]. Contactless devices can be divided into image and nonimage based systems, with image-based systems typically employing RGB or infrared cameras, whereas non-image based systems have typically employed modalities including time of flight sensors [29,30], acoustics [31], and radar [32,33]. While many new devices make claims of the ability to monitor patients for illness [34], there appears to be very little literature documenting their effectiveness in the unwell, let alone patients that are deteriorating.…”

Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

“…The calibration process is performed using subject-specific (intra-subject) scaling factors, learnt in a training phase from both depth-based and spirometer volume-time data. The medical achievements of our remote approaches were reported in [6,7]. Apart from our works [3][4][5][6][7], we know of only [8,9] which also performed remote respiratory assessment, rather than just breathing rate estimation or respiration monitoring [10][11][12][13][14][15].…”

“…The medical achievements of our remote approaches were reported in [6,7]. Apart from our works [3][4][5][6][7], we know of only [8,9] which also performed remote respiratory assessment, rather than just breathing rate estimation or respiration monitoring [10][11][12][13][14][15]. In [8,9], Ostadabbas et al detected airway obstruction as mild, moderate and severe, and computed only FEV1 in [8].…”

Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

“…In order to compute the scaling factors and PFT measures, several keypoints were automatically detected from the volume-time data. The medical significance of our remote lung function assessment approach has been reported in [16], [17].…”

“…Constraining the body's natural reaction to deep forced inhalation-exhalation can prevent subjects from performing their best breathing effort and would therefore affect their lung function measures. Unlike all previous remote approaches which restrict the subject's movement during their tests [5]- [17], our proposed method allows subjects to perform PFT as routine spirometry procedures without restricting the subject's natural body reactions at the inhalation-exhalation stages. Our contribution to the state-ofthe-art is therefore to facilitate remote respiratory monitoring and diagnosis without unduly constraining patients.…”