Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

Soleimani, Vahid; Mirmehdi, Majid; Damen, Dima; Dodd, James

doi:10.1109/icip.2018.8451202

Cited by 5 publications

(12 citation statements)

References 18 publications

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“…So the impact of body motion artifacts is huge for extracting the respiratory condition. Soleimani et al [33] extracted the chestsurface respiratory pattern by performing a principal component analysis (PCA) on temporal 3-D geometrical features extracted from the chest and posterior shape models to eliminate the body motion artifacts. But the artifacts elimination which completely based on the original signals still requires further exploration in our study.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The average error of 0.07 ± 0.06 liter for estimating the tidal volume (TV) and patients with airway obstruction (AO) were detected with 80% accuracy. 5) Soleimani et al [33] presented a vision-based trunkmotion tolerant approach which estimated lung volume-time data remotely in forced vital capacity (FVC) by using two opposing Kinects. After modelling of trunk shape, the chestsurface respiratory pattern can be computed on temporal geometrical features extracted from the chest and posterior shapes.…”

Section: Comparison With the State-of-the-art Approachesmentioning

confidence: 99%

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An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

Sun

Chen

et al. 2019

IEEE Access

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In order to obtain the respiratory condition unobtrusively and comfortably, a non-contact method based on the commercial depth camera Realsense SR300 was proposed to extract respiratory information from depth data. In this paper, a respiratory region detecting algorithm which is mainly based on the morphological method was proposed to obtain the region of interest (ROI) with the depth images. The proposed algorithm contains four steps: body edge extraction, noise reduction, ''image skeleton'' extraction, and respiratory region estimation. As a result, the respiratory waveform can be derived from the depth data in the ROI. For validation, experiments were carried out to verify the feasibility of obtaining the respiratory information with this approach. In consideration of different application scenarios, 20 kinds of conditions were designed and applied for the experiments. The respiratory rate extracted from the depth waveform can be calculated, and the accuracy achieved was 95.20% for all data while utilizing polysomnography thorax effort signal as gold standard. Through the Bland-Altman analysis, it represented that the proposed system had a good agreement (r 2 = 0.88) with the gold standard. In addition, the performances of the system in the 20 different conditions were analyzed by statistics, and the results showed that the system has good adaptability and robustness for different conditions. In conclusion, the proposed algorithm can fit different scenarios, and this paper provides a novel option for extracting the physiological information with depth data.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Comparison With the State-of-the-art Approachesmentioning

confidence: 99%

An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

Sun

Chen

et al. 2019

IEEE Access

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“…Especially during strong breathing maneuvers, the upper body may actively support the breathing movement. In this case, the entire upper body moves forward during inhalation and backward during exhalation [38]. These movements are suppressed when sitting or lying, which simultaneously raises the signalto-noise ratio (SNR) [7].…”

Section: Settingsmentioning

confidence: 99%

“…Seppanen et al 2015 [52] state to use two SROIs, defined as horizontal stripes at the xiphoid process near the umbilicus. With the help of a principal component analysis (PCA), many evenly distributed SROIs are combined into one respiratory signal in Soleimani et al 2018 [38] to reduce the influence of body movements. Yu et al 2012 [53] use three SROIs after manually applying a chest model.…”

Section: Roi-selectionmentioning

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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RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning

Sharshar,

Elhady

et al. 2024

IEEE Access

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Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

Cited by 5 publications

References 18 publications

An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

Depth-Based Measurement of Respiratory Volumes: A Review

RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning

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