Depth-Based Measurement of Respiratory Volumes: A Review

Wichum, Felix; Wiede, Christian; Seidl, Karsten

doi:10.3390/s22249680

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…In recent years, non-contact systems for chest wall movements detection have been explored, including motion capture systems, like optoelectronic plethysmography (OEP) [ 17 , 18 ] and depth cameras (e.g., structured light, time-of-flight) [ 19 , 20 , 21 ]. OEP is mainly used, and it is considered the gold standard for evaluating chest wall motions and detecting respiratory volumes.…”

Section: Introductionmentioning

confidence: 99%

Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study

Molinaro,

Schena,

Silvestri

et al. 2023

Sensors

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The identification of respiratory patterns based on the movement of the chest wall can assist in monitoring an individual’s health status, particularly those with neuromuscular disorders, such as hemiplegia and Duchenne muscular dystrophy. Thoraco-abdominal asynchrony (TAA) refers to the lack of coordination between the rib cage and abdominal movements, characterized by a time delay in their expansion. Motion capture systems, like optoelectronic plethysmography (OEP), are commonly employed to assess these asynchronous movements. However, alternative technologies able to capture chest wall movements without physical contact, such as RGB digital cameras and time-of-flight digital cameras, can also be utilized due to their accessibility, affordability, and non-invasive nature. This study explores the possibility of using a single RGB digital camera to record the kinematics of the thoracic and abdominal regions by placing four non-reflective markers on the torso. In order to choose the positions of these markers, we previously investigated the movements of 89 chest wall landmarks using OEP. Laboratory tests and volunteer experiments were conducted to assess the viability of the proposed system in capturing the kinematics of the chest wall and estimating various time-related respiratory parameters (i.e., fR, Ti, Te, and Ttot) as well as TAA indexes. The results demonstrate a high level of agreement between the detected chest wall kinematics and the reference data. Furthermore, the system shows promising potential in estimating time-related respiratory parameters and identifying phase shifts indicative of TAA, thus suggesting its feasibility in detecting abnormal chest wall movements without physical contact with a single RGB camera.

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Section: Introductionmentioning

confidence: 99%

Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study

Molinaro,

Schena,

Silvestri

et al. 2023

Sensors

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Wearable Devices for Respiratory Monitoring

Vicente,

Sebastião,

Sencadas

2024

Adv Funct Materials

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Respiratory diseases are currently monitored through traditional pulmonary function tests, such as spirometry. However, the restrictions of these procedures, particularly in the context of the COVID‐19 pandemic, have underscored the need for alternative approaches to respiratory health assessment. Wearable devices have emerged as a promising solution, providing continuous data collection, and overcoming the limitations posed by conventional methods. This review explores the multifaceted field of wearable devices for respiratory monitoring, presenting the most common sensing technologies applied to pulmonary ventilation, their constituent materials, fabrication techniques, and diverse morphologies to enhance sensor performance. The role of machine learning algorithms and ethical data sharing is highlighted, contributing to the forthcoming patient‐centered healthcare landscape. Ultimately, the importance of validation and calibration protocols for wearable devices is underlined. In anticipation of evolving healthcare needs, this in‐depth study addresses the current challenges in wearable respiratory monitoring while laying a robust foundation for a personalized, connected, and ethically sound future for respiratory care.

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Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study

Xu,

Shang,

Feng

et al. 2024

Respir Res

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Background The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML algorithms associated with the impulse oscillometry(IOS) analysis to aid in the diagnostic of respiratory changes in SAD. We also find out the best configuration for this task. Methods IOS and spirometry were measured in 280 subjects, including a healthy control group (n = 78), a group with normal spirometry (n = 158) and a group with abnormal spirometry (n = 44). Various supervised machine learning (ML) algorithms and feature selection strategies were examined, such as Support Vector Machines (SVM), Random Forests (RF), Adaptive Boosting (ADABOOST), Navie Bayesian (BAYES), and K-Nearest Neighbors (KNN). Results The first experiment of this study demonstrated that the best oscillometric parameter (BOP) was R5, with an AUC value of 0.642, when comparing a healthy control group(CG) with patients in the group without lung volume-defined SAD(PPFN). The AUC value of BOP in the control group was 0.769 compared with patients with spirometry defined SAD(PPFA) in the PPF population. In the second experiment, the ML technique was used. In CGvsPPFN, RF and ADABOOST had the best diagnostic results (AUC = 0.914, 0.915), with significantly higher accuracy compared to BOP (p < 0.01). In CGvsPPFA, RF and ADABOOST had the best diagnostic results (AUC = 0.951, 0.971) and significantly higher diagnostic accuracy (p < 0.01). In the third, fourth and fifth experiments, different feature selection techniques allowed us to find the best IOS parameters (R5, (R5-R20)/R5 and Fres). The results demonstrate that the performance of ADABOOST remained essentially unaltered following the application of the feature selector, whereas the diagnostic accuracy of the remaining four classifiers (RF, SVM, BAYES, and KNN) is marginally enhanced. Conclusions IOS combined with ML algorithms provide a new method for diagnosing SAD in subjects with chronic respiratory symptoms and PPF. The present study’s findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

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Depth-Based Measurement of Respiratory Volumes: A Review

Cited by 3 publications

References 69 publications

Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study

Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study

Wearable Devices for Respiratory Monitoring

Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study

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