Remote, Depth-Based Lung Function Assessment

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

doi:10.1109/tbme.2016.2618918

Cited by 21 publications

(45 citation statements)

References 34 publications

(66 reference statements)

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“…Further, a pneumotach must be calibrated before each session to be able to measure accurately. Thus, remote respiratory sensing has recently become very popular and numerous approaches have been proposed for lung function assessment [5], [6], respiration resistance [7], [8], and tidal volume respiratory monitoring and breathing rate estimation [9]- [15], based on time-of-flight…”

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confidence: 99%

“…This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ [5], [6], [8], [12], [13] and structured light depth sensors [7], [9]- [11] and RGB video cameras [14], [15]. Some of these are briefly considered in Section II.…”

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confidence: 99%

“…Among all the existing related studies, we are only aware of Ostadabbas et al [7], [8] and our previous works [5], [6], which performed clinical respiratory assessment. Ostadabbas et al [7], [8] mainly focused on airway resistance estimation and estimated two measures, FVC and FEV1, in [7].…”

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confidence: 99%

“…Ostadabbas et al [7], [8] mainly focused on airway resistance estimation and estimated two measures, FVC and FEV1, in [7]. In [6], we introduced a remote lung function assessment approach to estimate 11 PFT measures using a single depth sensor. The PFT measures were computed from a calibrated depth-based volume-time data, obtained by estimating the variation of chest volume per frame.…”

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confidence: 99%

“…Our previous approaches [5], [6] were based on a single depth sensor, which made them very sensitive to the subject's trunk motion during the PFT test. Although subjects were asked to be completely still, most of them inevitably moved their trunk, especially during the deep forced inhalation-exhalation.…”

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confidence: 99%

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Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Soleimani

Mirmehdi²,

Damen³

et al. 2018

IEEE Trans. Biomed. Eng.

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confidence: 99%

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Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Soleimani

Mirmehdi²,

Damen³

et al. 2018

IEEE Trans. Biomed. Eng.

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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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New intelligent network approach for monitoring physiological parameters: the case of Benin

et al. 2020

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Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients' hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems' characteristics and the patient physiological parameters' monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter's functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients' care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance.

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Remote, Depth-Based Lung Function Assessment

Cited by 21 publications

References 34 publications

Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

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

New intelligent network approach for monitoring physiological parameters: the case of Benin

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