Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Soleimani, Vahid; Mirmehdi, Majid; Damen, Dima; Camplani, Massimo; Hannuna, Sion; Sharp, Charles; Dodd, James

doi:10.1109/tbme.2017.2778157

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…With the use of a second camera, the measurement signal is calculated as the difference between the measurement signal of the front camera and the measurement signal of the rear camera. Thus, the entire upper body is viewed and artifacts caused by movements are reduced [43]. Later, the authors use PCA for motion artifact reduction.…”

Section: Signal Reconstructionmentioning

confidence: 99%

“…The principal component then corresponds to a motion-adjusted signal [38]. Empirical Mode Decomposition (EMD) [58] can be used for detrending data [43].…”

Section: Signal Reconstructionmentioning

confidence: 99%

“…Soleimani et al 2018 [43] compare a multiple-camera approach with a single-camera method. Mean and standard deviation are reported for forced vital capacity measurements and slow vital capacity measurements.…”

mentioning

confidence: 99%

“…The other datasets [48,57] of this working group restricted the active movement of the patient during the respiratory recordings, so that fewer errors occur [43].…”

mentioning

confidence: 99%

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Depth-Based Measurement of Respiratory Volumes: A Review

Wichum

Wiede

Seidl

2022

Sensors

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Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.

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Section: Signal Reconstructionmentioning

confidence: 99%

“…The principal component then corresponds to a motion-adjusted signal [38]. Empirical Mode Decomposition (EMD) [58] can be used for detrending data [43].…”

Section: Signal Reconstructionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The other datasets [48,57] of this working group restricted the active movement of the patient during the respiratory recordings, so that fewer errors occur [43].…”

mentioning

confidence: 99%

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Depth-Based Measurement of Respiratory Volumes: A Review

Wichum

Wiede

Seidl

2022

Sensors

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“…Most PPG-based respiration monitoring works have only reported respiratory rate, but not other parameters, because the measurement of respiration is indirect in this way. Detecting the subtle body motions induced by respiration. The targeted body parts could be chest, trunk, shoulders, or other places where the respiratory signal is sensitive enough to be captured by a regular camera − in 2D or an RGB-D depth camera − in 3D. Compared with indirect PPG-based respiration monitoring, directly tracking motion caused by respiration activities can provide more respiration information.…”

Section: Theoretical Basis Of Physiological Waveformsmentioning

confidence: 99%

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

2020

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Using a camera as an optical sensor to monitor physiological parameters has garnered considerable research interest in biomedical engineering in recent decades. Researchers have explored the use of a camera for monitoring a variety of physiological waveforms, together with the vital signs carried by these waveforms. Most of the obtained waveforms are related to the human respiratory and cardiovascular systems, and in addition of being indicative of overall health, they can also detect early signs of certain diseases. While using a camera for noncontact physiological signal monitoring offers the advantages of low cost and operational ease, it also has the disadvantages such as vulnerability to motion and lack of burden-free calibration solutions in some use cases. This study presents an overview of the existing camera-based methods that have been reported in recent years. It introduces the physiological principles behind these methods, signal acquisition approaches, various types of acquired signals, data processing algorithms, and application scenarios of these methods. It also discusses the technological gaps between the camera-based methods and traditional medical techniques, which are mostly contact-based. Furthermore, we present the manner in which noncontact physiological signal monitoring use has been extended, particularly over the recent years, to more day-to-day aspects of individuals’ lives, so as to go beyond the more conventional use case scenarios. We also report on the development of novel approaches that facilitate easier measurement of less often monitored and recorded physiological signals. These have the potential of ushering a host of new medical and lifestyle applications. We hope this study can provide useful information to the researchers in the noncontact physiological signal measurement community.

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Detection of respiratory activity in newborn infants using a noncontact vision‐based monitor

Eastwood‐Sutherland

Gale

et al. 2023

Pediatric Pulmonology

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Objective: To examine the effectiveness of a noncontact vision-based infrared respiratory monitor (IRM) in the detection of authentic respiratory motion in newborn infants.Study Design: Observational study in a neonatal intensive care unit.Methods: Eligible infants lay supine with torso exposed under the IRM's infrared depth-map camera and torso images were recorded at 30 frames/s. Respiratory motion waveforms were subsequently derived from upper (IRM upper ) and lower (IRM lower ) torso region images and compared with contemporaneous impedance pneumography (IP) and capsule pneumography (CP). Waveforms, in 15 s investigative epochs, were scanned with an 8 s sliding window for authentic respiratory waveform (spectral purity index [SPI] ≥ 0.75, minimum five complete breaths).Maximum SPI and frequency of occurrence of authentic respiratory waveform in 15 s epochs were compared between monitoring modalities in pooled and per patient data (Friedman ANOVA).Results: Recordings comprised 532 min of images from 35 infants, yielding 2131 investigative epochs, with authentic respiratory motion detected in all infants. For CP, IP, IRM upper , and IRM lower , the proportion of epochs containing authentic respiratory motion in pooled data were 65%, 50%, 36%, and 48%, with median SPI max of 0.79, 0.75, 0.70, and 0.74, respectively. Per-patient average SPI max was 0.79, 0.75, 0.69, and 0.74 for CP, IP, IRM upper , and IRM lower with proportion of authentic respiratory motion being 64%, 50%, 29%, and 49%, respectively. Conclusion:An IRM focused on the lower torso detected authentic respiratory motion with comparable performance to IP in newborn infants in intensive care and deserves further investigation.

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Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Abstract: The proposed dPPG method remarkably drops the error mean and standard deviation of FEF , FEF , FEF, IC , and ERV measures by half, compared to the single Kinect approach. These significant improvements establish the potential for unconstrained remote respiratory monitoring and diagnosis.

Cited by 15 publications

References 26 publications

Depth-Based Measurement of Respiratory Volumes: A Review

Depth-Based Measurement of Respiratory Volumes: A Review

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

Detection of respiratory activity in newborn infants using a noncontact vision‐based monitor

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