Sensors

2024

Jump to: 2022, 2021, 2020

12 pages, 15147 KiB

Open AccessArticle

Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds

by Walid Ashraf and Zahra Moussavi

Sensors 2024, 24(17), 5511; https://doi.org/10.3390/s24175511 - 26 Aug 2024

Viewed by 1026

Abstract

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This [...] Read more.

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice. Full article

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2022

Jump to: 2024, 2021, 2020

17 pages, 2591 KiB

Open AccessReview

Depth-Based Measurement of Respiratory Volumes: A Review

by Felix Wichum, Christian Wiede and Karsten Seidl

Sensors 2022, 22(24), 9680; https://doi.org/10.3390/s22249680 - 10 Dec 2022

Cited by 3 | Viewed by 5632

Abstract

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 [...] Read more.

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. Full article

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17 pages, 3884 KiB

Open AccessArticle

Free-Breathing Phase-Resolved Oxygen-Enhanced Pulmonary MRI Based on 3D Stack-of-Stars UTE Sequence

by Pengfei Xu, Jichang Zhang, Zhen Nan, Thomas Meersmann and Chengbo Wang

Sensors 2022, 22(9), 3270; https://doi.org/10.3390/s22093270 - 24 Apr 2022

Cited by 2 | Viewed by 2743

Abstract

Compared with hyperpolarized noble gas MRI, oxygen-enhanced lung imaging is a cost-effective approach to investigate lung function. In this study, we investigated the feasibility of free-breathing phase-resolved oxygen-enhanced pulmonary MRI based on a 3D stack-of-stars ultra-short echo time (UTE) sequence. We conducted both [...] Read more.

Compared with hyperpolarized noble gas MRI, oxygen-enhanced lung imaging is a cost-effective approach to investigate lung function. In this study, we investigated the feasibility of free-breathing phase-resolved oxygen-enhanced pulmonary MRI based on a 3D stack-of-stars ultra-short echo time (UTE) sequence. We conducted both computer simulation and in vivo experiments and calculated percent signal enhancement maps of four different respiratory phases on four healthy volunteers from the end of expiration to the end of inspiration. The phantom experiment was implemented to verify simulation results. The respiratory phase was segmented based on the extracted respiratory signal and sliding window reconstruction, providing phase-resolved pulmonary MRI. Demons registration algorithm was applied to compensate for respiratory motion. The mean percent signal enhancement of the average phase increases from anterior to posterior region, matching previous literature. More details of pulmonary tissues were observed on post-oxygen inhalation images through the phase-resolved technique. Phase-resolved UTE pulmonary MRI shows the potential as a valuable method for oxygen-enhanced MRI that enables the investigation of lung ventilation on middle states of the respiratory cycle. Full article

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30 pages, 65176 KiB

Open AccessEditor’s ChoiceReview

Respiratory Monitoring by Ultrafast Humidity Sensors with Nanomaterials: A Review

by Shinya Kano, Nutpaphat Jarulertwathana, Syazwani Mohd-Noor, Jerome K. Hyun, Ryota Asahara and Harutaka Mekaru

Sensors 2022, 22(3), 1251; https://doi.org/10.3390/s22031251 - 7 Feb 2022

Cited by 37 | Viewed by 5778

Abstract

Respiratory monitoring is a fundamental method to understand the physiological and psychological relationships between respiration and the human body. In this review, we overview recent developments on ultrafast humidity sensors with functional nanomaterials for monitoring human respiration. Key advances in design and materials [...] Read more.

Respiratory monitoring is a fundamental method to understand the physiological and psychological relationships between respiration and the human body. In this review, we overview recent developments on ultrafast humidity sensors with functional nanomaterials for monitoring human respiration. Key advances in design and materials have resulted in humidity sensors with response and recovery times reaching 8 ms. In addition, these sensors are particularly beneficial for respiratory monitoring by being portable and noninvasive. We systematically classify the reported sensors according to four types of output signals: impedance, light, frequency, and voltage. Design strategies for preparing ultrafast humidity sensors using nanomaterials are discussed with regard to physical parameters such as the nanomaterial film thickness, porosity, and hydrophilicity. We also summarize other applications that require ultrafast humidity sensors for physiological studies. This review provides key guidelines and directions for preparing and applying such sensors in practical applications. Full article

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2021

Jump to: 2024, 2022, 2020

14 pages, 1623 KiB

Open AccessArticle

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

by Youngbeen Chung, Jie Jin, Hyun In Jo, Hyun Lee, Sang-Heon Kim, Sung Jun Chung, Ho Joo Yoon, Junhong Park and Jin Yong Jeon

Sensors 2021, 21(21), 7036; https://doi.org/10.3390/s21217036 - 23 Oct 2021

Cited by 5 | Viewed by 5311

Abstract

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed [...] Read more.

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability. Full article

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17 pages, 3244 KiB

Open AccessArticle

Respiration Monitoring via Forcecardiography Sensors

by Emilio Andreozzi, Jessica Centracchio, Vincenzo Punzo, Daniele Esposito, Caitlin Polley, Gaetano D. Gargiulo and Paolo Bifulco

Sensors 2021, 21(12), 3996; https://doi.org/10.3390/s21123996 - 9 Jun 2021

Cited by 35 | Viewed by 6207

Abstract

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic [...] Read more.

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R² = 0.98) vs. 0.98, 0.11 s (R² = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases. Full article

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11 pages, 1026 KiB

Open AccessArticle

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 4238

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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19 pages, 5037 KiB

Open AccessArticle

Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor

by Ala’aldeen Al-Halhouli, Loiy Al-Ghussain, Osama Khallouf, Alexander Rabadi, Jafar Alawadi, Haipeng Liu, Khaled Al Oweidat, Fei Chen and Dingchang Zheng

Sensors 2021, 21(2), 468; https://doi.org/10.3390/s21020468 - 11 Jan 2021

Cited by 16 | Viewed by 6222

Abstract

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease that causes long-term breathing problems. The reliable monitoring of respiratory rate (RR) is very important for the treatment and management of COPD. Based on inkjet printing technology, we have developed a stretchable and [...] Read more.

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease that causes long-term breathing problems. The reliable monitoring of respiratory rate (RR) is very important for the treatment and management of COPD. Based on inkjet printing technology, we have developed a stretchable and wearable sensor that can accurately measure RR on normal subjects. Currently, there is a lack of comprehensive evaluation of stretchable sensors in the monitoring of RR on COPD patients. We aimed to investigate the measurement accuracy of our sensor on COPD patients. Methodology: Thirty-five patients (Mean ± SD of age: 55.25 ± 13.76 years) in different stages of COPD were recruited. The measurement accuracy of our inkjet-printed (IJPT) sensor was evaluated at different body postures (i.e., standing, sitting at 90°, and lying at 45°) on COPD patients. The RR recorded by the IJPT sensor was compared with that recorded by the reference e-Health sensor using paired T-test and Wilcoxon signed-rank test. Analysis of variation (ANOVA) was performed to investigate if there was any significant effect of individual difference or posture on the measurement error. Statistical significance was defined as p-value less than 0.05. Results: There was no significant difference between the RR measurements collected by the IJPT sensor and the e-Health reference sensor overall and in three postures (p > 0.05 in paired T-tests and Wilcoxon signed-rank tests). The sitting posture had the least measurement error of −0.0542 ± 1.451 bpm. There was no significant effect of posture or individual difference on the measurement error or relative measurement error (p > 0.05 in ANOVA). Conclusion: The IJPT sensor can accurately measure the RR of COPD patients at different body postures, which provides the possibility for reliable monitoring of RR on COPD patients. Full article

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2020

Jump to: 2024, 2022, 2021

11 pages, 4016 KiB

Open AccessLetter

Breath-Jockey: Development and Feasibility Assessment of a Wearable System for Respiratory Rate and Kinematic Parameter Estimation for Gallop Athletes

by Joshua Di Tocco, Riccardo Sabbadini, Luigi Raiano, Federica Fani, Simone Ripani, Emiliano Schena, Domenico Formica and Carlo Massaroni

Sensors 2021, 21(1), 152; https://doi.org/10.3390/s21010152 - 29 Dec 2020

Cited by 12 | Viewed by 3391

Abstract

In recent years, wearable devices for physiological parameter monitoring in sports and physical activities have been gaining momentum. In particular, some studies have focused their attention on using available commercial monitoring systems mainly on horses during training sessions or competitions. Only a few [...] Read more.

In recent years, wearable devices for physiological parameter monitoring in sports and physical activities have been gaining momentum. In particular, some studies have focused their attention on using available commercial monitoring systems mainly on horses during training sessions or competitions. Only a few studies have focused on the jockey’s physiological and kinematic parameters. Although at a glance, it seems jockeys do not make a lot of effort during riding, it is quite the opposite. Indeed, especially during competitions, they profuse a short but high intensity effort. To this extend, we propose a wearable system integrating conductive textiles and an M-IMU to simultaneously monitor the respiratory rate (RR) and kinematic parameters of the riding activity. Firstly, we tested the developed wearable system on a healthy volunteer mimicking the typical riding movements of jockeys and compared the performances with a reference instrument. Lastly, we tested the system on two gallop jockeys during the “

137^{\circ}

Derby Italiano di Galoppo”. The proposed system is able to track both the RR and the kinematic parameters during the various phases of the competition both at rest and during the race. Full article

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11 pages, 2163 KiB

Open AccessLetter

Derivation of Respiratory Metrics in Health and Asthma

by Joseph Prinable, Peter Jones, David Boland, Alistair McEwan and Cindy Thamrin

Sensors 2020, 20(24), 7134; https://doi.org/10.3390/s20247134 - 12 Dec 2020

Cited by 5 | Viewed by 3242

Abstract

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine [...] Read more.

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates. Full article

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12 pages, 4480 KiB

Open AccessLetter

Non-Contact Respiratory Measurement Using a Depth Camera for Elderly People

by Wakana Imano, Kenichi Kameyama, Malene Hollingdal, Jens Refsgaard, Knud Larsen, Cecilie Topp, Sissel Højsted Kronborg, Josefine Dam Gade and Birthe Dinesen

Sensors 2020, 20(23), 6901; https://doi.org/10.3390/s20236901 - 3 Dec 2020

Cited by 8 | Viewed by 2915

Abstract

Measuring respiration at home for cardiac patients, a simple method that can detect the patient’s natural respiration, is needed. The purpose of this study was to develop an algorithm for estimating the tidal volume (TV) and respiratory rate (RR) from the depth value [...] Read more.

Measuring respiration at home for cardiac patients, a simple method that can detect the patient’s natural respiration, is needed. The purpose of this study was to develop an algorithm for estimating the tidal volume (TV) and respiratory rate (RR) from the depth value of the chest and/or abdomen, which were captured using a depth camera. The data of two different breathing patterns (normal and deep) were acquired from both the depth camera and the spirometer. The experiment was performed under two different clothing conditions (undressed and wearing a T-shirt). Thirty-nine elderly volunteers (male = 14) were enrolled in the experiment. The TV estimation algorithm for each condition was determined by regression analysis using the volume data from the spirometer as the objective variable and the depth motion data from the depth camera as the explanatory variable. The RR estimation was calculated from the peak interval. The mean absolute relative errors of the estimated TV for males were 14.0% under undressed conditions and 10.7% under T-shirt-wearing conditions; meanwhile, the relative errors for females were 14.7% and 15.5%, respectively. The estimation error for the RR was zero out of a total of 206 breaths under undressed conditions and two out of a total of 218 breaths under T-shirt-wearing conditions for males. Concerning females, the error was three out of a total of 329 breaths under undressed conditions and five out of a total of 344 breaths under T-shirt-wearing conditions. The developed algorithm for RR estimation was accurate enough, but the estimated occasionally TV had large errors, especially in deep breathing. The cause of such errors in TV estimation is presumed to be a result of the whole-body motion and inadequate setting of the measurement area. Full article

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46 pages, 2708 KiB

Open AccessFeature PaperEditor’s ChoiceReview

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

by Andrea Nicolò, Carlo Massaroni, Emiliano Schena and Massimo Sacchetti

Sensors 2020, 20(21), 6396; https://doi.org/10.3390/s20216396 - 9 Nov 2020

Cited by 228 | Viewed by 32197

Abstract

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these [...] Read more.

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal. Full article

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84 pages, 20580 KiB

Open AccessReview

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

by Erik Vanegas, Raul Igual and Inmaculada Plaza

Sensors 2020, 20(18), 5446; https://doi.org/10.3390/s20185446 - 22 Sep 2020

Cited by 80 | Viewed by 11620

Abstract

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this [...] Read more.

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system. Full article

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19 pages, 3194 KiB

Open AccessArticle

A Wearable Device for Breathing Frequency Monitoring: A Pilot Study on Patients with Muscular Dystrophy

by Ambra Cesareo, Santa Aurelia Nido, Emilia Biffi, Sandra Gandossini, Maria Grazia D’Angelo and Andrea Aliverti

Sensors 2020, 20(18), 5346; https://doi.org/10.3390/s20185346 - 18 Sep 2020

Cited by 13 | Viewed by 3852

Abstract

Patients at risk of developing respiratory dysfunctions, such as patients with severe forms of muscular dystrophy, need a careful respiratory assessment, and periodic follow-up visits to monitor the progression of the disease. In these patients, at-home continuous monitoring of respiratory activity patterns could [...] Read more.

Patients at risk of developing respiratory dysfunctions, such as patients with severe forms of muscular dystrophy, need a careful respiratory assessment, and periodic follow-up visits to monitor the progression of the disease. In these patients, at-home continuous monitoring of respiratory activity patterns could provide additional understanding about disease progression, allowing prompt clinical intervention. The core aim of the present study is thus to investigate the feasibility of using an innovative wearable device for respiratory monitoring, particularly breathing frequency variation assessment, in patients with muscular dystrophy. A comparison of measurements of breathing frequency with gold standard methods showed that the device based on the inertial measurement units (IMU-based device) provided optimal results in terms of accuracy errors, correlation, and agreement. Participants positively evaluated the device for ease of use, comfort, usability, and wearability. Moreover, preliminary results confirmed that breathing frequency is a valuable breathing parameter to monitor, at the clinic and at home, because it strongly correlates with the main indexes of respiratory function. Full article

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16 pages, 6746 KiB

Open AccessLetter

Simple Wireless Impedance Pneumography System for Unobtrusive Sensing of Respiration

by Pablo Aqueveque, Britam Gómez, Emyrna Monsalve, Enrique Germany, Paulina Ortega-Bastidas, Sebastián Dubo and Esteban J. Pino

Sensors 2020, 20(18), 5228; https://doi.org/10.3390/s20185228 - 14 Sep 2020

Cited by 18 | Viewed by 8702

Abstract

This extended paper presents the development and implementation at a prototype level of a wireless, low-cost system for the measurement of the electrical bioimpedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure impedance pneumography. The [...] Read more.

This extended paper presents the development and implementation at a prototype level of a wireless, low-cost system for the measurement of the electrical bioimpedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure impedance pneumography. The measurement device works for impedance measurements ranging from 1 Ω to 1800 Ω. Fifteen volunteers were measured with the prototype. We found that the left hemithorax has higher impedance compared to the right hemithorax, and the acquired signal presents the phases of the respiratory cycle with variations between 1 Ω, in normal breathing, to 6 Ω in maximum inhalation events. The system can measure the respiratory cycle variations simultaneously in both hemithorax with a mean error of −0.18 ± 1.42 BPM (breaths per minute) in the right hemithorax and −0.52 ± 1.31 BPM for the left hemithorax, constituting a useful device for the breathing rate calculation and possible screening applications. Full article

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15 pages, 2995 KiB

Open AccessArticle

Wearable Belt With Built-In Textile Electrodes for Cardio—Respiratory Monitoring

by Emanuele Piuzzi, Stefano Pisa, Erika Pittella, Luca Podestà and Silvia Sangiovanni

Sensors 2020, 20(16), 4500; https://doi.org/10.3390/s20164500 - 12 Aug 2020

Cited by 34 | Viewed by 4278

Abstract

Unobtrusive and continuous monitoring of vital signs is becoming more and more important both for patient monitoring in the home environment and for sports activity tracking. Even though many gadgets and clinical systems exist, the need for simple, low-cost and easily applicable solutions [...] Read more.

Unobtrusive and continuous monitoring of vital signs is becoming more and more important both for patient monitoring in the home environment and for sports activity tracking. Even though many gadgets and clinical systems exist, the need for simple, low-cost and easily applicable solutions still remains, especially in view of a more widespread use within everyone’s reach. The paper presents a fully wearable and wireless sensorized belt, suitable to simultaneously acquire respiratory and cardiac signals employing a single acquisition channel. The adopted method relies on a 50-kHz current injected in the subject thorax through a couple of textile electrodes and on envelope detection of the trans-thoracic voltage acquired from a couple of different embedded electrodes. The resulting signal contains both the baseband electrocardiogram (ECG) signal and the trans-thoracic impedance signal, which encodes respiratory acts. The two signals can be easily separated through suitable filtering and the cardio–respiratory rates extracted. The proposed solution yields performances comparable to those of a spirometer and a two-lead ECG. The whole system, with a realization cost below 100 €, a wireless interface, and several hours (or even days) of autonomy, is a suitable candidate for everyday use, especially if complemented by motion artifact removal techniques, currently under implementation. Full article

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13 pages, 1593 KiB

Open AccessLetter

Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

by Ruisheng Lei, Bingo Wing-Kuen Ling, Peihua Feng and Jinrong Chen

Sensors 2020, 20(11), 3238; https://doi.org/10.3390/s20113238 - 6 Jun 2020

Cited by 17 | Viewed by 4234

Abstract

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble [...] Read more.

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate. Full article

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17 pages, 4209 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers

by Daniela Lo Presti, Arianna Carnevale, Jessica D’Abbraccio, Luca Massari, Carlo Massaroni, Riccardo Sabbadini, Martina Zaltieri, Joshua Di Tocco, Marco Bravi, Sandra Miccinilli, Silvia Sterzi, Umile G. Longo, Vincenzo Denaro, Michele A. Caponero, Domenico Formica, Calogero M. Oddo and Emiliano Schena

Sensors 2020, 20(2), 536; https://doi.org/10.3390/s20020536 - 18 Jan 2020

Cited by 67 | Viewed by 6152

Abstract

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These [...] Read more.

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively. Full article

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13 pages, 3024 KiB

Open AccessArticle

Development of a Brain–Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography

by Chang-Hee Han, Euijin Kim and Chang-Hwan Im

Sensors 2020, 20(2), 348; https://doi.org/10.3390/s20020348 - 8 Jan 2020

Cited by 10 | Viewed by 3522

Abstract

Asynchronous brain–computer interfaces (BCIs) based on electroencephalography (EEG) generally suffer from poor performance in terms of classification accuracy and false-positive rate (FPR). Thus, BCI toggle switches based on electrooculogram (EOG) signals were developed to toggle on/off synchronous BCI systems. The conventional BCI toggle [...] Read more.

Asynchronous brain–computer interfaces (BCIs) based on electroencephalography (EEG) generally suffer from poor performance in terms of classification accuracy and false-positive rate (FPR). Thus, BCI toggle switches based on electrooculogram (EOG) signals were developed to toggle on/off synchronous BCI systems. The conventional BCI toggle switches exhibit fast responses with high accuracy; however, they have a high FPR or cannot be applied to patients with oculomotor impairments. To circumvent these issues, we developed a novel BCI toggle switch that users can employ to toggle on or off synchronous BCIs by holding their breath for a few seconds. Two states—normal breath and breath holding—were classified using a linear discriminant analysis with features extracted from the respiration-modulated photoplethysmography (PPG) signals. A real-time BCI toggle switch was implemented with calibration data trained with only 1-min PPG data. We evaluated the performance of our PPG switch by combining it with a steady-state visual evoked potential-based BCI system that was designed to control four external devices, with regard to the true-positive rate and FPR. The parameters of the PPG switch were optimized through an offline experiment with five subjects, and the performance of the switch system was evaluated in an online experiment with seven subjects. All the participants successfully turned on the BCI by holding their breath for approximately 10 s (100% accuracy), and the switch system exhibited a very low FPR of 0.02 false operations per minute, which is the lowest FPR reported thus far. All participants could successfully control external devices in the synchronous BCI mode. Our results demonstrated that the proposed PPG-based BCI toggle switch can be used to implement practical BCIs. Full article

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