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Keywords = optoelectronic plethysmography

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23 pages, 7592 KiB  
Article
Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation
by Liangwen Yan, Ze Long, Jie Qian, Jianhua Lin, Sheng Quan Xie and Bo Sheng
Sensors 2024, 24(9), 2925; https://doi.org/10.3390/s24092925 - 3 May 2024
Viewed by 1601
Abstract
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration [...] Read more.
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network–Long Short-Term Memory–Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system’s promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance. Full article
(This article belongs to the Special Issue Multi-sensor Fusion in Medical Imaging, Diagnosis and Therapy)
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<p>The framework of the stroke assessment system.</p>
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<p>The framework of the PPG acquisition system.</p>
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<p>‘NeuroPulseGuard’: (<b>a</b>) physical diagram of the fusion-based PPG sampling device; (<b>b</b>) front view of the internal PCB board of the fusion-based sampling device; (<b>c</b>) schematic diagram of the reverse side of the internal PCB board of the fusion-based sampling device.</p>
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<p>Operational diagram of the multi-functional sensor MAX30101.</p>
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<p>Data processing: (<b>a</b>) raw PPG signal (10 min); (<b>b</b>) filtered PPG signal (10 min).</p>
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<p>Data processing details: (<b>a</b>) raw PPG signal (10 s); (<b>b</b>) PPG signal after Chebyshev Type II filtering (10 s); (<b>c</b>) PPG signal after removing baseline drift (10 s). The enlarged view of a specific detail in the PPG signal is shown within the green circle.</p>
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<p>(<b>a</b>) MCNN-LSTM-Attention model; (<b>b</b>) MCNN module; (<b>c</b>) classifier module.</p>
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<p>Fusion diagram of MCNN. In the “shallow feature” graph and the “deep feature” graph, different colors represent different features.</p>
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<p>Diagram of LSTM multi-signal fusion structure.</p>
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<p>Abstract Encoder–Decoder framework.</p>
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<p>Photo of PPG signal acquisition of a patient in the hospital.</p>
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<p>Ten-fold cross-validation.</p>
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<p>(<b>a</b>) Training: accuracy curve. (<b>b</b>) Training: loss curve.</p>
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<p>Confusion matrix.</p>
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<p>Comparison of the internal structures of the devices: (<b>a</b>) Wei’s equipment; (<b>b</b>) ‘NeuroPulseGuard’ device.</p>
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15 pages, 6261 KiB  
Article
Limb Volume Measurements: A Comparison of Circumferential Techniques and Optoelectronic Systems against Water Displacement
by Giovanni Farina, Manuela Galli, Leonardo Borsari, Andrea Aliverti, Ioannis Th. Paraskevopoulos and Antonella LoMauro
Bioengineering 2024, 11(4), 382; https://doi.org/10.3390/bioengineering11040382 - 15 Apr 2024
Cited by 1 | Viewed by 1446
Abstract
Background. Accurate measurements of limb volumes are important for clinical reasons. We aimed to assess the reliability and validity of two centimetric and two optoelectronic techniques for limb volume measurements against water volumetry, defined as the gold standard. Methods. Five different measurement methods [...] Read more.
Background. Accurate measurements of limb volumes are important for clinical reasons. We aimed to assess the reliability and validity of two centimetric and two optoelectronic techniques for limb volume measurements against water volumetry, defined as the gold standard. Methods. Five different measurement methods were executed on the same day for each participant, namely water displacement, fixed-height (circumferences measured every 5 (10) cm for the upper (lower limb) centimetric technique, segmental centimetric technique (circumferences measured according to proportional height), optoelectronic plethysmography (OEP, based on a motion analysis system), and IGOODI Gate body scanner technology (which creates an accurate 3D avatar). Results. A population of 22 (15 lower limbs, 11 upper limbs, 8 unilateral upper limb lymphoedema, and 6 unilateral lower limb lymphoedema) participants was selected. Compared to water displacement, the fixed-height centimetric method, the segmental centimetric method, the OEP, and the IGOODI technique resulted in mean errors of 1.2, 0.86, −16.0, and 0.71%, respectively. The corresponding slopes (and regression coefficients) of the linear regression lines were 1.0002 (0.98), 1.0047 (0.99), 0.874 (0.94) and 0.9966 (0.99). Conclusion. The centimetric methods and the IGOODI system are accurate in measuring limb volume with an error of <2%. It is important to evaluate new objective and reliable techniques to improve diagnostic and follow-up possibilities. Full article
(This article belongs to the Special Issue Optical Techniques for Biomedical Engineering)
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<p>Experimental setup of the different measures of the lower limb: water volumetry (<b>top left panel</b>, please notice the patient in need of help to keep the position), circumferential techniques in orthostatism (<b>top middle panel</b>) and clinostatism (<b>top right panel</b>), optoelectronic plethysmography (<b>bottom left panel</b>) and the corresponding 3D marker reconstruction (<b>bottom middle panel</b>), and 3D avatar (i.e., a virtual twin of the patient) from the IGOODI Gate technology (<b>bottom right panel</b>).</p>
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<p>Experimental setup of the different measures of the upper limb: water volumetry (<b>top left panel</b>), circumferential techniques in orthostatism (<b>top middle panel</b>) and clinostatism (<b>top right panel</b>), optoelectronic plethysmography (<b>bottom left panel</b>) and the corresponding 3D marker reconstruction (<b>bottom middle panel</b>), and 3D avatar (i.e., a virtual twin of the patient) created by the IGOODI Gate technology (<b>bottom right panel</b>).</p>
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<p>Lower (<b>left panel</b>) and upper (<b>right panel</b>) limb detection points identified using the segmental technique. The patients signed written informed consent for the publication of their photos.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the fixed-height technique on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the fixed-height technique and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the segmental technique on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the segmental technique and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p><b>(Left</b>) Linear correlation graph, with the segmental technique on the x-axis and the optoelectronic plethysmography data on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between optoelectronic plethysmography and the fixed-height technique. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Red: clinostatism. Cyan: orthostatism.</p>
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<p>(<b>Left</b>) Linear correlation graph, with water displacement on the x-axis and the IGOODI system on the y-axis. Short dashed line: the interpolating line of the data. (<b>Right</b>) Bland-Altman graph, with the average between the two measurements on the x-axis and the difference between the IGOODI system and water displacement. Solid line: mean difference. Short-dashed lines: mean difference ±2 standard deviations. Circle: lower limb. Square: upper limb. Cyan: orthostatism.</p>
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17 pages, 3296 KiB  
Article
Efficacy of Marker-Based Motion Capture for Respiratory Cycle Measurement: A Comparison with Spirometry
by Natalia D. Shamantseva, Tatiana A. Klishkovskaia, Sergey S. Ananyev, Andrey Y. Aksenov and Tatiana R. Moshonkina
Sensors 2023, 23(24), 9736; https://doi.org/10.3390/s23249736 - 10 Dec 2023
Cited by 1 | Viewed by 1282
Abstract
Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) [...] Read more.
Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) based on a motion capture video system. Two analysis methods, the summation method and the triangle method were developed. The study focused on determining the optimal number of markers while achieving accuracy in respiratory parameter measurements. The results showed that most analysis methods showed a difference of ≤0.5 breaths per minute, with R2 ≥ 0.94 (p < 0.001) compared to spirometry. The best OEP methods for respiratory rate were the abdominal triangles and the sum of abdominal markers in all body positions. The study explored inspiratory and expiratory durations. The research found that 5–9 markers were sufficient to accurately determine respiratory time components in all body positions, reducing the marker requirements compared to previous studies. This interchangeability of OEP methods with standard spirometry demonstrates the potential of non-invasive methods for the simultaneous assessment of body segment movements, center of pressure dynamics, and respiratory movements. Future research is required to improve the clinical applicability of these methods. Full article
(This article belongs to the Special Issue Sensors for Breathing Monitoring)
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<p>The placement of all markers used in the study; <a href="#sensors-23-09736-t001" class="html-table">Table 1</a> gives a detailed description of each marker.</p>
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<p>Methods for calculating, processing, and extracting respiratory curves were developed using optoelectronic plethysmography. (<b>A</b>): Filtering, smoothing, and normalization of the signal. (<b>B</b>): Spectrum constructed to show how the maximum peak area was selected for calculations, and table with peak area-to-total area ratios. (<b>C</b>): Extraction of respiratory curves using summation and triangle methods. MoCap: motion-captured. PSD: power spectral density.</p>
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<p>Scatter plots to visualize the relationship between breathing rate determined by spirometry (horizontal axis) and optoelectronic plethysmography (vertical axis), in breaths per minute. (<b>A</b>) Sitting, (<b>B</b>) standing, and (<b>C</b>) supine positions. Each data point represents one participant.</p>
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<p>Scatter plots to visualize the relationship between inspiratory time determined by spirometry (horizontal axis) and optoelectronic plethysmography (vertical axis), in s. (<b>A</b>) Sitting, (<b>B</b>) standing, and (<b>C</b>) supine positions. Each data point represents one participant.</p>
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<p>Scatter plots to visualize the relationship between expiratory time determined by spirometry (horizontal axis) and optoelectronic plethysmography (vertical axis), in s. (<b>A</b>) Sitting, (<b>B</b>) standing, and (<b>C</b>) supine positions. Each data point represents one participant.</p>
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<p>Selected best OEP analysis methods for BR, T<sub>ins</sub> and T<sub>exp</sub> in sitting, standing, and supine positions are solidly coloured. Methods with R<sup>2</sup> &gt; 0.80 are shown as transparent bars. n—is the number of markers required for measurement.</p>
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18 pages, 5175 KiB  
Article
Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study
by Nunzia Molinaro, Emiliano Schena, Sergio Silvestri and Carlo Massaroni
Sensors 2023, 23(15), 6960; https://doi.org/10.3390/s23156960 - 5 Aug 2023
Cited by 1 | Viewed by 1454
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Collection Biomedical Imaging and Sensing)
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<p>(<b>A</b>) Schematic representation of the chest wall with the orientation of the three axes (i.e., <span class="html-italic">x</span>, <span class="html-italic">y</span>, and <span class="html-italic">z</span>). (<b>B</b>) Volume and displacements signal along the <span class="html-italic">y</span>-axis per one marker: (a) represents the <math display="inline"><semantics><mrow><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><msub><mrow><mi>d</mi></mrow><mrow><mi>r</mi></mrow></msub><mfenced separators="|"><mrow><msub><mrow><mi>l</mi><mi>o</mi><mi>c</mi></mrow><mrow><mi>m</mi><mi>i</mi><mi>n</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></mfenced></mrow></semantics></math> corresponding to the minimum point on the volume signal; and (b) is the <math display="inline"><semantics><mrow><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><msub><mrow><mi>d</mi></mrow><mrow><mi>r</mi></mrow></msub><mfenced separators="|"><mrow><msub><mrow><mi>l</mi><mi>o</mi><mi>c</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></mfenced></mrow></semantics></math> corresponding to the maximum point on the volume signal.</p>
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<p>Bar plots of the mean displacements of the 42 markers along the <span class="html-italic">x-</span>, <span class="html-italic">y-</span>, and <span class="html-italic">z</span>-axes with the corresponding uncertainty for all the subjects.</p>
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<p>Representation of the four compartments of the chest wall (i.e., right thorax—RTh, right abdomen—Rab, left thorax—LTh, and left abdomen—Lab).</p>
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<p>(<b>A</b>) Bar plots of the mean percentage weight expressed by each marker for each compartment. (<b>B</b>) Schematic representation of the anterior chest wall with highlighted the positions of the non-reflective markers (in red). The red arrows identify the photo-reflective markers used as reference to place the non-reflective ones.</p>
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<p>Bar plot of the mean reprojection error per image obtained during the calibration procedure.</p>
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<p>Mean value of the MAPE in percentage for each non-reflective marker during the three simulated time intervals (i.e., t = 3 s, t = 1.5 s, and t = 1 s).</p>
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<p>Experimental setup composed of eight cameras of the OEP system, the photo-reflective markers, the iPhone 8 camera, and the four non-reflective markers.</p>
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<p>Example for one subject of the respiratory waveform retrieved from the video against the reference volume for each compartment.</p>
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<p>Scheme of the procedure adopted to extract temporal parameters from both the respiratory signals obtained from the non-contact system and the OEP. The procedure was performed for each compartment.</p>
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<p>(<b>A</b>) Plot of the thoracic and abdominal compartments signals with the corresponding figure of Lissajous of the six respiratory acts, where <span class="html-italic">m</span> is the volume displaced by the abdomen at 50% of the thorax volume, while <span class="html-italic">s</span> represents the total volume displaced by the abdomen. (<b>B</b>) Example of a single respiratory act used to compute PS, where <span class="html-italic">a</span> and <span class="html-italic">b</span> are the time points at which the change in the direction of the thorax and the abdomen signals occur.</p>
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<p>Normalized marker’s displacements extracted from the video against the reference displacements for each compartment per subject. In magenta are the non-reflective markers used in the proposed system.</p>
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<p>Bland–Altman plots comparing <span class="html-italic">f<sub>R</sub></span>, <span class="html-italic">T<sub>i</sub></span>, <span class="html-italic">T<sub>e</sub></span>, and <span class="html-italic">T<sub>tot</sub></span> estimated from respiratory signals obtained from a video.</p>
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<p>PA and PS results are presented as boxplots, where the center line is the median, and box limits indicate the 25th (lower limit) and 75th percentiles (upper limit). The lines above and below the box limits represent the largest and smallest values, respectively. The red + identify the outlier values.</p>
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11 pages, 1670 KiB  
Article
The Effect of Active Stretching Training in Patients with Chronic Venous Insufficiency Monitored by Raster-Stereography
by Erica Menegatti, Simona Mandini, Anselmo Pagani, Beatrice Mandini, Valentina Zerbini, Tommaso Piva, Andrea Raisi, Marinella Fabbri, Marco Fogli, Gianni Mazzoni, Paolo Zamboni and Sergio Gianesini
Sensors 2022, 22(21), 8509; https://doi.org/10.3390/s22218509 - 4 Nov 2022
Cited by 2 | Viewed by 3482
Abstract
(1) Background: Musculoskeletal disorders can be associated with advanced clinical stages of chronic venous insufficiency (CVI). The aim of the study is to investigate the effect of active stretching (AS) training on lower limb venous function and quality of life in patients affected [...] Read more.
(1) Background: Musculoskeletal disorders can be associated with advanced clinical stages of chronic venous insufficiency (CVI). The aim of the study is to investigate the effect of active stretching (AS) training on lower limb venous function and quality of life in patients affected by CVI. (2) Methods: A prospective two-armed pilot randomized controlled was conducted. Twenty (20) CVI patients were randomly assigned to an AS training or to a control group (C) who did not receive any exercise indication. At baseline and after three months all the participants were tested for leg volumetry (LV), air plethysmography (APG), and quality of life (QoL) measured by a disease specific validated questionnaire (VVSymQ), ankle range of motion (ROM), and postural deformities using an optoelectronic body posture machine. (3) Results: At the end of the training in the AS group a significant leg volume reduction was detected (from 2340 ± 239 mL to 2239 ± 237 mL (4.3%); p < 0.0001), whereas in the C group no significant volume changes were found. The ejection fraction rate (EF%) increased significantly from 49.3 ± 9.3 to 61.1 ± 14.5, p < 0.005. A moderate-strong linear correlation with EF% and ankle ROM variation was found (R2 = 0.6790; p < 0.0034). Several postural outcomes such as pelvic tilt, pelvic torsion, and lordotic angle significantly improved in the AS group (p < 0.01, p < 0.04, p < 0.01 respectively). (4) Conclusion: The AS training impacts on the APG parameters related to the musculoskeletal pump efficiency, opening a further possibility in the management of CVI patients by means of an appropriate adapted physical exercise program. Full article
(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)
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<p>Example of active stretching exercise protocol (the letters(<b>A</b>–<b>X</b>) are referred to each exercise reported in <a href="#sensors-22-08509-t001" class="html-table">Table 1</a>).</p>
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<p>Flow diagram of the progress through the study phases according to the CONSORT 2010 statement.</p>
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<p>Leg volume significant reduction after three months follow-up in AS group (white histogram), while no significant changes in leg volume were recorded in the control group (dotted grey histogram).</p>
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<p>Correlation between ankle ROM and EF% variations in the AS exercise group.</p>
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22 pages, 13544 KiB  
Article
Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers
by Jorge Herranz Olazábal, Fokko Wieringa, Evelien Hermeling and Chris Van Hoof
Sensors 2022, 22(16), 6059; https://doi.org/10.3390/s22166059 - 13 Aug 2022
Cited by 6 | Viewed by 2698
Abstract
Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time [...] Read more.
Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R2 values of rPPG with rSPG (R2 = 0.99) and ITA (R2 = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG. Full article
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<p>Measurement setup diagram.</p>
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<p>Setup diagram with sample 639 nm raw images in reflective (<b>left</b>) and transmissive mode (<b>right</b>). Left top: reflective image with LED illumination. Left bottom: reflective image with laser illumination (clear speckle pattern visible). Right top: transmissive image with LED illumination. Right bottom: transmissive image with laser illumination.</p>
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<p>Raw camera video frames are captured at 200 fps and by time-domain-multiplexing split-up into 4 channels, each pulsing a different light source at 50 fps. Two different processing methods—amplitude domain and spatial domain—are both applied to the same video stream. Here, we show rPPG versus rSPG for Channel 1. Note that raw waveforms are displayed vertically inverted, as is common practice for commercial PPG and SPG signal outputs.</p>
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<p>Expansion of the intensity pattern in transmission measurements causing spatial variability changes originated from absorption changes together with rPPG and rSPG. (<b>A</b>) Raw video image. (<b>B</b>) False color-coded intensity image. (<b>C</b>) Systolic (t = 183.1 s) portion of the false color-coded image above a certain intensity threshold. (<b>D</b>) Diastolic (t = 183.9 s) portion of the false color-coded image above the same intensity threshold. This area is somewhat expanded compared with C. (<b>E</b>) ITA pulsation. (<b>F</b>) Transmission rPPG. (<b>G</b>) Transmission rSPG. Note that raw waveforms are displayed vertically inverted, as is common practice for commercial PPG and SPG signal outputs. PPG and SPG synchronous signals from a random subject with 1200 μs integration time.</p>
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<p>Reflective mode. Raw synchronous R-rPPG (<b>left</b>) and R-rPPG (<b>right</b>) signals extracted from the 4 multiplexed light sources (1200 µs integration time). As expected, good SPG quality was observed when laser light sources are used. Note that raw waveforms are displayed without vertical inversion—PPG and SPG synchronous signals from a random subject with 1200 µs integration time.</p>
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<p>Reflective mode boxplots of quality index for R-rPPG signals with 4 different lights at 5 integration times. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + and ‡ symbols represent outliers.</p>
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<p>Reflective mode boxplots of quality index for R-rSPG signals with 4 different lights at 5 integration times in the reflection. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Transmissive mode. Synchronous rPPG (<b>left</b>) and rSPG (<b>right</b>) signals extracted from the 4 multiplexed light sources. Note the strong similarity in waveform morphology, as opposed to the reflective mode results in <a href="#sensors-22-06059-f004" class="html-fig">Figure 4</a>. Note that raw waveforms are displayed without vertical inversion—PPG and SPG synchronous signals from a random subject with 1200 µs integration time.</p>
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<p>Boxplots of quality index of rPPG signals with 4 different lights at 5 integration times in transmission. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Boxplots of quality index of rSPG signals with 4 different lights at 5 integration times in transmission. Note the higher signal quality of 850 nm “SPG” signal when using LEDs instead of lasers. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Coherent mode. Synchronous rPPG (<b>left</b>) and rSPG (<b>right</b>) signals extracted from the 4 multiplexed Laser light sources (1200 µs integration time). Note the differences in waveform morphology in reflective mode, versus the similarities in transmissive mode. Note that raw waveforms are displayed without vertical inversion—PPG and SPG synchronous signals from a random subject with 1200 µs integration time.</p>
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<p>Boxplots of quality index of rPPG signals with 4 different lights at 5 integration times with coherent light in transmission and reflection. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Boxplots of quality index of rSPG signals with 4 different lights at 5 integration times with coherent light in transmission and reflection. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Non-coherent mode. Synchronous rPPG (<b>left</b>) and rSPG (<b>right</b>) signals extracted from the 4 multiplexed LED light sources (1200 µs integration time). Note that raw waveforms are displayed without vertical inversion—PPG and SPG synchronous signals from a random subject with 1200 µs integration time.</p>
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<p>Boxplots of quality index of rPPG signals with 4 different lights at 5 integration times with non-coherent light in transmission and reflection. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Boxplots of quality index of rSPG signals with 4 different lights at 5 integration times with non-coherent light in transmission and reflection. The inner box divisions indicate: Q1 (the first quartile) to Q3 (the third quartile). The median is marked by a line across the box. The “whiskers” indicate results from Q1 and Q3 to the most extreme data points. The + symbols represent outliers.</p>
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<p>Linear regression of rPPG and rSPG data with a 95% prediction interval.</p>
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<p>Linear regression of rPPG and ITA data with a 95% prediction interval.</p>
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<p>Comparison between SQI scales used in this study and a sophisticated widely used SQI template specifically developed for PPG. The template PPG SQI works better for PPG, although it does not work well for SPG. * Note the SPG values marked with an asterisk, which score as medium and low with our SQI scale, whereas both scored the same 0.47 value with the template SQI method.</p>
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20 pages, 2175 KiB  
Article
Assessing Respiratory Activity by Using IMUs: Modeling and Validation
by Vito Monaco, Carolina Giustinoni, Tommaso Ciapetti, Alessandro Maselli and Cesare Stefanini
Sensors 2022, 22(6), 2185; https://doi.org/10.3390/s22062185 - 11 Mar 2022
Cited by 7 | Viewed by 2582
Abstract
This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of [...] Read more.
This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach. Full article
(This article belongs to the Section Wearables)
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<p>The left panel (<b>a</b>) shows the grid adopted to uniformly span the anterior trunk wall with reflective markers. Horizontal lines are craniocaudally identified as follows: A connects the lateral ends of both clavicles, B is equally distant from A and C, C connects the nipples, D crosses the xiphoid process, E connects the lower costal margins, F crosses the umbilicus, and G connects the anterior iliac spines. Vertical lines are identified as follows: K and O cross the right and left anterior iliac spines, respectively; M is the midline crossing the umbilicus; L and N cross the nipples; and J and Pare right and left midaxillary lines, respectively. The right panel (<b>b</b>) shows the adopted marker set. Note that markers are located on the nodes of the grid shown in the left panel except for those related to line A, which are positioned along the clavicles. The panel also shows the location of the nine IMUs (orange boxes) and their orientation (see x- and y-axes in the bottom right corner).</p>
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<p>Summary of the algorithm that we used to identify the principal components (PCs) that best matched the time course of respiratory volumes (i.e., V<sub>CW</sub>, V<sub>RC</sub>, and V<sub>AB</sub>). For this representative example, all data refer to the first repetition of subject #5. In addition, data reported in Panel (<b>A</b>) were computed from matrix A<sub>X</sub>. The red box in Panel (<b>A</b>) identifies the PC that best matches (in this representative case) respiratory volumes according to the Pearson correlation coefficient. The volumes shown in Panel (<b>B</b>) are shifted along the vertical axis to set their minimum values in the whole data stream to 0. The valleys and peaks refer to onset of the inhalation and exhalation phases, respectively. The data reported in Panel (<b>C</b>) were z-scored (see apex * in the legend) to make the comparison straightforward.</p>
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<p>The three panels depict the variance explained by the best-matching retained PCs related to volumes V<sub>CW</sub>, V<sub>RC</sub>, and V<sub>AB</sub>, respectively. Bars and error bars respectively refer to mean and standard deviation of the explained variance across subjects. Data reported in magenta and blue refer to male and female. The label * represents the outcome of the unpaired <span class="html-italic">t</span>-test when significant (i.e., <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The subplots show the weight coefficients relating IMU outputs (from left to right: homologous data matrices A<sub>ToT</sub>, A<sub>X</sub>, and A<sub>Y</sub>) to retained PCs that best match with respiratory patterns (from top to bottom: V<sub>CW</sub>, V<sub>RC</sub>, and V<sub>AB</sub>). The bars and error bars refer to the averaged and standard deviations across subjects, respectively.</p>
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<p>From top to bottom, the panels show a representative example of the tidal volume measured by optoelectronic plethysmography (<b>black curves</b>) and estimated by both <span class="html-italic">Model 1</span> (<b>red curves</b>) and <span class="html-italic">Model 2</span> (<b>green curves</b>) and related to the chest wall (V<sub>CW</sub>), rib cage (V<sub>RC</sub>), and abdominal (V<sub>AB</sub>) compartments.</p>
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<p>From top to bottom, the panels show mean and one standard deviation error bar for all respiratory variables (i.e., RR, DI, DE, VI, and VE), as measured by optoelectronic plethysmography, across compartments (i.e., CW, RC, and AB) for both female and male groups (pink and blue, respectively).</p>
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<p>From top to bottom, the panels show the error percentage (mean and one standard deviation error bar) concerning all assessed respiratory variables (i.e., RR, DI, DE, VI, and VE) related to IMU-based <span class="html-italic">Model 1</span> (on the <b>left</b>) and <span class="html-italic">Model 2</span> (on the <b>right</b>) for both female and male groups (pink and blue, respectively).</p>
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21 pages, 667 KiB  
Review
Assessing the Tidal Volume through Wearables: A Scoping Review
by Vito Monaco and Cesare Stefanini
Sensors 2021, 21(12), 4124; https://doi.org/10.3390/s21124124 - 16 Jun 2021
Cited by 19 | Viewed by 3556
Abstract
The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal [...] Read more.
The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal volume during both daily activities and clinical settings. A screening of the literature (Pubmed, Scopus, and Web of Science) was carried out in December 2020 to collect studies: i. comparing one or more methodological approaches for the assessment of tidal volume with the outcome of a state-of-the-art measurement device (i.e., spirometry or optoelectronic plethysmography); ii. dealing with technological solutions designed to be exploited in wearable devices. From the initial 1031 documents, only 36 citations met the eligibility criteria. These studies highlighted that the tidal volume can be estimated by using different technologies ranging from IMUs to strain sensors (e.g., resistive, capacitive, inductive, electromagnetic, and optical) or acoustic sensors. Noticeably, the relative volumetric error of these solutions during quasi-static tasks (e.g., resting and sitting) is typically ≥10% but it deteriorates during dynamic motor tasks (e.g., walking). As such, additional efforts are required to improve the performance of these devices and to identify possible applications based on their accuracy and reliability. Full article
(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)
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<p>Flow chart of the article selection process.</p>
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11 pages, 1026 KiB  
Article
Novel Real-Time OEP Phase Angle Feedback System for Dysfunctional Breathing Pattern Training—An Acute Intervention Study
by Carol M. E. Smyth, Samantha L. Winter and John W. Dickinson
Sensors 2021, 21(11), 3714; https://doi.org/10.3390/s21113714 - 26 May 2021
Cited by 6 | Viewed by 4232
Abstract
Dysfunctional breathing patterns (DBP) can have an impact on an individual’s quality of life and/or exercise performance. Breathing retraining is considered to be the first line of treatment to correct breathing pattern, for example, reducing ribcage versus abdominal movement asynchrony. Optoelectronic plethysmography (OEP) [...] Read more.
Dysfunctional breathing patterns (DBP) can have an impact on an individual’s quality of life and/or exercise performance. Breathing retraining is considered to be the first line of treatment to correct breathing pattern, for example, reducing ribcage versus abdominal movement asynchrony. Optoelectronic plethysmography (OEP) is a non-invasive 3D motion capture technique that measures the movement of the chest wall. The purpose of this study was to investigate if the use of a newly developed real-time OEP phase angle and volume feedback system, as an acute breathing retraining intervention, could result in a greater reduction of phase angle values (i.e., an improvement in movement synchrony) when compared to real-time OEP volume feedback alone. Eighteen individuals with a DBP performed an incremental cycle test with OEP measuring chest wall movement. Participants were randomly assigned to either the control group, which included the volume-based OEP feedback or to the experimental group, which included both the volume-based and phase angle OEP feedback. Participants then repeated the same cycle test using the real-time OEP feedback. The phase angle between the ribcage versus abdomen (RcAbPhase), between the pulmonary ribcage and the combined abdominal ribcage and abdomen (RCpAbPhase), and between the abdomen and the shoulders (AbSPhase) were calculated during both cycle tests. Significant increases in RcAbPhase (pre: −2.89°, post: −1.39°, p < 0.01), RCpAbPhase (pre: −2.00°, post: −0.50°, p < 0.01), and AbSPhase (pre: −2.60°, post: −0.72°, p < 0.01) were found post-intervention in the experimental group. This indicates that the experimental group demonstrated improved synchrony in their breathing pattern and therefore, reverting towards a healthy breathing pattern. This study shows for the first time that dysfunctional breathing patterns can be acutely improved with real-time OEP phase angle feedback and provides interesting insight into the feasibility of using this novel feedback system for breathing pattern retraining in individuals with DBP. Full article
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<p>OEP real-time feedback system displayed for: (<b>a</b>) the control group with Qualisys Track Manager attaching the AIM model to the 90 markers and the real-time total volume trace plot streamed via MATLAB; (<b>b</b>) the experimental group with the additional real-time Konno–Mead breath loop for the phase angle between the ribcage and abdomen streamed via MATLAB.</p>
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<p>Example of OEP signal processing for phase angle calculation: (<b>a</b>) total volume trace calculated from the 90 markers using the prism-based method [<a href="#B33-sensors-21-03714" class="html-bibr">33</a>]; (<b>b</b>) division of the total volume into the ribcage and abdomen compartmental volumes; (<b>c</b>) Konno–Mead loop representing the phase angle between the ribcage and abdomen (RcAbPhase). m is the width of the loop at 50% of the ribcage displacement, s is the range of abdomen displacement, and phase angle is calculated as arcsin (m/s).</p>
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<p>Division of the OEP marker set (anterior view) into: (<b>a</b>) the ribcage (blue) and abdomen (red) for the phase angle RcAbPhase; (<b>b</b>) the pulmonary ribcage (blue) and the combined abdominal ribcage and abdomen (red) for the phase angle RCpAbPhase; (<b>c</b>) the shoulders (blue) and the abdomen (red) for the phase angle AbSPhase.</p>
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<p>Comparison of breathing parameters between the control and experimental group at: (<b>a</b>) rest; (<b>b</b>) high intensity exercise; and (<b>c</b>) recovery post exercise pre- and post- acute breathing retraining intervention. * represents significant post hoc contrasts between pre- and post-intervention conditions within a given group with <span class="html-italic">p</span> &lt; 0.05.</p>
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24 pages, 3649 KiB  
Article
Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System
by Ambra Cesareo, Ylenia Previtali, Emilia Biffi and Andrea Aliverti
Sensors 2019, 19(1), 88; https://doi.org/10.3390/s19010088 - 27 Dec 2018
Cited by 48 | Viewed by 7546
Abstract
Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm [...] Read more.
Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best fB estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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<p>Block Diagram of the Analysis algorithm that allows derivation of breathing temporal parameters (f<sub>B</sub>. T<sub>I</sub>, T<sub>E</sub>) from quaternion-based orientation change signals recorded on Thorax, Abdomen and Reference point.</p>
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<p>Dimension-reduction block in detail. Starting from the 4 components [q<sub>0</sub>, q<sub>1</sub>, q<sub>2</sub>, q<sub>3</sub>] of each quaternion (Abdominal: Ab and Thoracic: Th), three methods are applied to obtain a single-component signal: two methods based on best quaternion component selection (“Area” and “Peak”) and one method based on the fusion of the 4 components through Principal Component Analysis (PCA). “Area” method selects the quaternion component with the larger area under the Power Spectral Density (PSD) estimate, while “Peak” method selects the quaternion component with the highest PSD’s peak. PCA-fusion method selects the first principal component (PC_1) that accounts for the largest variance in the data.</p>
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<p>Experimental setup. Retroreflective-marker configuration for optoelectronic plethysmography (OEP) and IMU-unit (Ab: Abdomen, Th: Thorax, Ref: Reference) placement in supine (A and B panels) and seated (C and D panels) positions. Panel E shows the experimental setup and the OEP Lab; Infrared cameras of the motion capture system are also noticeable.</p>
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<p>Relative errors (E%) of estimation of breathing frequency (<b>a</b>,<b>b</b>) and Duty Cycle (<b>c</b>,<b>d</b>) in supine (<b>a</b>,<b>c</b>) and seated (<b>b</b>,<b>d</b>) positions, computed for each method (Peak, Area and PCA) and for the “Ideal” component with respect to the reference (OEP). Errors are computed both for the Thoracic and abdominal compartments. Horizontal blue lines indicate statistical significance of difference (post-hoc analysis, Wilcoxon test FDR corrected).</p>
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<p>Comparisons of breathing frequency (f<sub>B</sub> expressed as breaths/minuteute) measurements by using the proposed device and by using Optoelectronic plethysmography (OEP) presented as regression analysis, in supine (top panels) and seated (bottom panels) positions. For what concerns f<sub>B</sub> measurements obtained with the IMU-device, three dimension-reduction methods were considered: Area, Peak and PCA-fusion. The performance obtained by using these three methods is benchmarked against that obtained with the Ideal quaternion component determined a posteriori based on the minimum estimation error. The regression line between measurements done by OEP and the proposed device is plotted, and the relative equation presented, both for the thorax and the abdomen.</p>
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<p>Comparisons of inspiratory time (T<sub>I</sub> expressed as seconds) measurements by using the proposed device and by using Optoelectronic plethysmography (OEP) presented as regression analysis, in supine (top panels) and seated (bottom panels) positions. For what concerns T<sub>I</sub> measurements obtained with the IMU-device, three dimension-reduction methods were considered: Area, Peak and PCA-fusion. The performance obtained by using these three methods is benchmarked against that obtained with the Ideal quaternion component determined a posteriori based on the minimum estimation error. The regression line between measurements done by OEP and the proposed device is plotted, and the relative equation presented, both for the thorax and the abdomen.</p>
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<p>Comparisons of expiratory time (T<sub>E</sub>, expressed as seconds) measurements by using the proposed device and by using Optoelectronic plethysmography (OEP) presented as regression analysis, in supine (top panels) and seated (bottom panels) positions. Regarding T<sub>E</sub> measurements obtained with the IMU-device, three dimension-reduction methods were considered. Area, Peak and PCA-fusion. The performance obtained by using these three methods is benchmarked against that obtained with the Ideal quaternion component determined a posteriori based on the minimum estimation error. The regression line between measurements done by OEP and the proposed device is plotted, and the relative equation presented, both for the thorax and the abdomen.</p>
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<p>Agreement analysis between OEP and the IMU-based device for breathing frequency (f<sub>B</sub>, expressed as breaths/minuteute) measurements, in supine (top panels) and seated (bottom panels) position. In each Bland–Altman plot the differences between measurements of f<sub>B</sub> obtained by using the IMU-based device and by using OEP are plotted against the mean of the two measurements. For homoscedastic data, the mean of the differences (bias: —) and limits of agreement (black dotted line) from mean − 1.96 s to mean + 1.96 s are represented by lines parallel to the X axis. For heteroscedastic data, the proportional bias (—) is represented by the ordinary least squares (OLS) line of best fit for the difference on mean values; V-shaped upper and lower 95% confidence limits (- - -) are calculated according to Bland [<a href="#B44-sensors-19-00088" class="html-bibr">44</a>].</p>
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<p>Agreement analysis between OEP and the IMU-based device for inspiratory time (T<sub>I</sub>, [s]) measurements, in supine (top panels) and seated (bottom panels) position. In each Bland–Altman plot the differences between measurements of T<sub>I</sub> obtained by using the IMU-based device and by using OEP are plotted against the mean of the two measurements. For homoscedastic data, the mean of the differences (bias: —) and limits of agreement (- - -) from mean − 1.96 s to mean + 1.96 s are represented by lines parallel to the X axis. For heteroscedastic data, the proportional bias (—) is represented by the OLS line of best fit for differences on mean values; V-shaped upper and lower 95% confidence limits (- - -) are calculated according to Bland [<a href="#B44-sensors-19-00088" class="html-bibr">44</a>].</p>
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<p>Agreement analysis between OEP and the IMU-based device for expiratory time (T<sub>E</sub>, [s]) measurements, in supine (top panels) and seated (bottom panels) position. In each Bland–Altman plot the differences between measurements of T<sub>E</sub> obtained by using the IMU-based device and by using OEP are plotted against the mean of the two measurements. For homoscedastic data, the mean of the differences (bias: —) and limits of agreement (- - -) from mean − 1.96 s to mean + 1.96 s are represented by lines parallel to the X axis. For heteroscedastic data, the proportional bias (—) is represented by the OLS line of best fit for differences on mean values; V-shaped upper and lower 95% confidence limits (- - -) are calculated according to Bland [<a href="#B44-sensors-19-00088" class="html-bibr">44</a>].</p>
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<p>Relative frequencies of quaternion component (q<sub>0</sub>, q<sub>1</sub>, q<sub>2</sub>, q<sub>3</sub>) selection using Area and Peak methods and of quaternion component selection as Ideal component, in supine and seated position. Each portion of the rings represents the ratio between the number of times that each quaternion component has been selected (by Area and Peak methods or as Ideal component respectively) and the total number of trials (n = 74).</p>
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1150 KiB  
Article
Smart Textile Based on Fiber Bragg Grating Sensors for Respiratory Monitoring: Design and Preliminary Trials
by Marco Ciocchetti, Carlo Massaroni, Paola Saccomandi, Michele A. Caponero, Andrea Polimadei, Domenico Formica and Emiliano Schena
Biosensors 2015, 5(3), 602-615; https://doi.org/10.3390/bios5030602 - 14 Sep 2015
Cited by 121 | Viewed by 9804
Abstract
Continuous respiratory monitoring is important to assess adequate ventilation. We present a fiber optic-based smart textile for respiratory monitoring able to work during Magnetic Resonance (MR) examinations. The system is based on the conversion of chest wall movements into strain of two fiber [...] Read more.
Continuous respiratory monitoring is important to assess adequate ventilation. We present a fiber optic-based smart textile for respiratory monitoring able to work during Magnetic Resonance (MR) examinations. The system is based on the conversion of chest wall movements into strain of two fiber Bragg grating (FBG) sensors, placed on the upper thorax (UT). FBGs are glued on the textile by an adhesive silicon rubber. To increase the system sensitivity, the FBGs positioning was led by preliminary experiments performed using an optoelectronic system: FBGs placed on the chest surface experienced the largest strain during breathing. System performances, in terms of respiratory period (TR), duration of inspiratory (TI) and expiratory (TE) phases, as well as left and right UT volumes, were assessed on four healthy volunteers. The comparison of results obtained by the proposed system and an optoelectronic plethysmography highlights the high accuracy in the estimation of TR, TI, and TE: Bland-Altman analysis shows mean of difference values lower than 0.045 s, 0.33 s, and 0.35 s for TR, TI, and TE, respectively. The mean difference of UT volumes between the two systems is about 8.3%. The promising results foster further development of the system to allow routine use during MR examinations.Continuous respiratory monitoring is important to assess adequate ventilation. We present a fiber optic-based smart textile for respiratory monitoring able to work during Magnetic Resonance (MR) examinations. The system is based on the conversion of chest wall movements into strain of two fiber Bragg grating (FBG) sensors, placed on the upper thorax (UT). FBGs are glued on the textile by an adhesive silicon rubber. To increase the system sensitivity, the FBGs positioning was led by preliminary experiments performed using an optoelectronic system: FBGs placed on the chest surface experienced the largest strain during breathing. System performances, in terms of respiratory period (TR), duration of inspiratory (TI) and expiratory (TE) phases, as well as left and right UT volumes, were assessed on four healthy volunteers. The comparison of results obtained by the proposed system and an optoelectronic plethysmography highlights the high accuracy in the estimation of TR, TI, and TE: Bland-Altman analysis shows mean of difference values lower than 0.045 s, 0.33 s, and 0.35 s for TR, TI, and TE, respectively. The mean difference of UT volumes between the two systems is about 8.3%. The promising results foster further development of the system to allow routine use during MR examinations. Full article
(This article belongs to the Special Issue Optical Sensors for Biomedical Applications)
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<p>(<b>a</b>) FBGs position and distance between the two FBGs. Blue lines and markers identify the upper thorax right compartment, red lines and markers identify the upper thorax left compartment, green lines and markers identify the line which separates the two compartments; (<b>b</b>) trend of FBG distance during quiet breathing of a healthy subject.</p>
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<p>Picture of the experimental set-up.</p>
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<p>(<b>a</b>) Trend of the data provided by the OEP; (<b>b</b>) Trend of the data provided by the two FBGs; (<b>c</b>) three different parameters investigated: Respiratory periods, calculated as the time interval between two consecutive peaks, inspiratory periods, calculated as the time interval that elapses between a maximum and the previous minimum of the signal, and expiratory periods, calculated as the time interval that elapses between a minimum and the previous maximum.</p>
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<p>(<b>a</b>,<b>b</b>) Bland Altman plot comparing the respiratory period measured by OEP and by the smart textiles with the automatic method and the manual one, respectively; (<b>c</b>,<b>d</b>) Bland Altman plot comparing the inspiratory period measured by OEP and by the smart textiles with the automatic method and the manual one, respectively; (<b>e</b>,<b>f</b>) Bland Altman plot comparing the expiratory period measured by OEP and by the smart textiles with the automatic method and the manual one, respectively.</p>
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<p>Correlation between the FBGs wavelength changes and UT volume considering both left and right side. The best fitting lines are also shown.</p>
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<p>Comparison between the FBGs wavelength changes and UT volume considering both left and right side. The best fitting lines are also shown.</p>
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485 KiB  
Review
Optoelectronic Plethysmography has Improved our Knowledge of Respiratory Physiology and Pathophysiology
by Isabella Romagnoli, Barbara Lanini, Barbara Binazzi, Roberto Bianchi, Claudia Coli, Loredana Stendardi, Francesco Gigliotti and Giorgio Scano
Sensors 2008, 8(12), 7951-7972; https://doi.org/10.3390/s8127951 - 5 Dec 2008
Cited by 29 | Viewed by 15941
Abstract
It is well known that the methods actually used to track thoraco-abdominal volume displacement have several limitations. This review evaluates the clinical usefulness of measuring chest wall kinematics by optoelectronic plethysmography [OEP]. OEP provides direct measurements (both absolute and its variations) of the [...] Read more.
It is well known that the methods actually used to track thoraco-abdominal volume displacement have several limitations. This review evaluates the clinical usefulness of measuring chest wall kinematics by optoelectronic plethysmography [OEP]. OEP provides direct measurements (both absolute and its variations) of the volume of the chest wall and its compartments, according to the model of Ward and Macklem, without requiring calibration or subject cooperation. The system is non invasive and does not require a mouthpiece or nose-clip which may modify the pattern of breathing, making the subject aware of his breathing. Also, the precise assessment of compartmental changes in chest wall volumes, combined with pressure measurements, provides a detailed description of the action and control of the different respiratory muscle groups and assessment of chest wall dynamics in a number of physiological and clinical experimental conditions. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy)
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<p>89 marker model for respiratory acquisition. 42 markers are placed in front and 47 on the back of the subject.</p>
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<p>The three compartment chest wall model: A: Pulmonary apposed rib cage [RC,p]; B: abdominal apposed rib cage [RC,a]; C: abdomen [AB]; A+B+C = chest wall [CW].</p>
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323 KiB  
Review
Optoelectronic Plethysmography—A New Technic to Measure Changes of Chest Wall Volume
by Agnieszka Skoczylas and Paweł Śliwiński
Adv. Respir. Med. 2007, 75(1), 81-87; https://doi.org/10.5603/ARM.28012 - 13 Apr 2007
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Abstract
Optoelectronic plethysmography (OEP) is a new, noninvasive diagnostic tool that allows to measure changes of chest wall volume and its three compartments. Mathematical basis of the method, elements of the system and possibilities of combining OEP with other recording techniques used in pneumonology [...] Read more.
Optoelectronic plethysmography (OEP) is a new, noninvasive diagnostic tool that allows to measure changes of chest wall volume and its three compartments. Mathematical basis of the method, elements of the system and possibilities of combining OEP with other recording techniques used in pneumonology were discussed in details. OEP applications, results of the latest investigations and development perspectives were briefly presented. Full article
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