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Hong W. Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model. ACS APPLIED MATERIALS & INTERFACES 2024; 16:47337-47347. [PMID: 39192683 DOI: 10.1021/acsami.4c11493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.
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Affiliation(s)
- Wonki Hong
- Department of Digital Healthcare, Daejeon University, Daejeon 34520, Republic of Korea
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2
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Ashraf W, Moussavi Z. Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds. SENSORS (BASEL, SWITZERLAND) 2024; 24:5511. [PMID: 39275422 PMCID: PMC11397743 DOI: 10.3390/s24175511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 08/11/2024] [Accepted: 08/23/2024] [Indexed: 09/16/2024]
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 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.
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Affiliation(s)
- Walid Ashraf
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Zahra Moussavi
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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Lin Z, Duan S, Liu M, Dang C, Qian S, Zhang L, Wang H, Yan W, Zhu M. Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306880. [PMID: 38015990 DOI: 10.1002/adma.202306880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/22/2023] [Indexed: 11/30/2023]
Abstract
Sound plays a crucial role in the perception of the world. It allows to communicate, learn, and detect potential dangers, diagnose diseases, and much more. However, traditional acoustic sensors are limited in their form factors, being rigid and cumbersome, which restricts their potential applications. Recently, acoustic sensors have made significant advancements, transitioning from rudimentary forms to wearable devices and smart everyday clothing that can conform to soft, curved, and deformable surfaces or surroundings. In this review, the latest scientific and technological breakthroughs with insightful analysis in materials, physics, design principles, fabrication strategies, functions, and applications of flexible and wearable acoustic sensing technology are comprehensively explored. The new generation of acoustic sensors that can recognize voice, interact with machines, control robots, enable marine positioning and localization, monitor structural health, diagnose human vital signs in deep tissues, and perform organ imaging is highlighted. These innovations offer unique solutions to significant challenges in fields such as healthcare, biomedicine, wearables, robotics, and metaverse. Finally, the existing challenges and future opportunities in the field are addressed, providing strategies to advance acoustic sensing technologies for intriguing real-world applications and inspire new research directions.
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Affiliation(s)
- Zhiwei Lin
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore
| | - Shengshun Duan
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore
| | - Mingyang Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore
| | - Chao Dang
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore
| | - Shengtai Qian
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore
| | - Luxue Zhang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
| | - Hailiang Wang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
| | - Wei Yan
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
| | - Meifang Zhu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China
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Yook S, Kim D, Gupte C, Joo EY, Kim H. Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity. Sleep Med 2024; 114:211-219. [PMID: 38232604 PMCID: PMC10872216 DOI: 10.1016/j.sleep.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 12/28/2023] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. METHODS An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. RESULTS Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. CONCLUSIONS The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.
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Affiliation(s)
- Soonhyun Yook
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
| | - Dongyeop Kim
- Department of Neurology, Seoul Hospital, College of Medicine, Ewha Womans University, Seoul, 07804, South Korea
| | - Chaitanya Gupte
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, 06351, South Korea.
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA.
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Im S, Kim T, Min C, Kang S, Roh Y, Kim C, Kim M, Kim SH, Shim K, Koh JS, Han S, Lee J, Kim D, Kang D, Seo S. Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention. PLoS One 2023; 18:e0294447. [PMID: 37983213 PMCID: PMC10659186 DOI: 10.1371/journal.pone.0294447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 10/23/2023] [Indexed: 11/22/2023] Open
Abstract
This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.
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Affiliation(s)
- Sunghoon Im
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Taewi Kim
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | | | - Sanghun Kang
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Yeonwook Roh
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Changhwan Kim
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Minho Kim
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Seung Hyun Kim
- Department of Medical Humanities, Korea University College of Medicine, Seoul, Republic of Korea
| | - KyungMin Shim
- Industry-University Cooperation Foundation, Seogyeong University, Seoul, Republic of Korea
| | - Je-sung Koh
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Seungyong Han
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - JaeWang Lee
- Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Dohyeong Kim
- University of Texas at Dallas, Richardson, TX, United States of America
| | - Daeshik Kang
- Department of Mechanical Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - SungChul Seo
- Department of Nano-Chemical, Biological and Environmental Engineering, Seogyeong University, Seoul, Republic of Korea
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Rennoll V, McLane I, Eisape A, Grant D, Hahn H, Elhilali M, West JE. Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring. ACS APPLIED BIO MATERIALS 2023; 6:3241-3256. [PMID: 37470762 PMCID: PMC10804910 DOI: 10.1021/acsabm.3c00359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.
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Affiliation(s)
- Valerie Rennoll
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Ian McLane
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Adebayo Eisape
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Drew Grant
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Helena Hahn
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - James E West
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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7
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Li W, Deng X, Wang R, Meng S. Temporal convolution network based novel parallel disaggregation method for non-intrusive monitoring of the appliances’ consumptions in residential buildings. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Energy or load disaggregation, as one essential part of non-intrusive load monitoring (NILM), is an efficient way to separate the consumption information of target appliances from the whole consumption data, and can accordingly help to regulate people’s energy consumption behaviors. However, the consumptions of the target appliances are usually affected by the variance of the opening time, working condition and user interference, so it is a difficult task to realize precise disaggregation. To further improve the energy disaggregation accuracy, this paper proposes a new parallel disaggregation strategy with two subnets for the energy consumption disaggregation of the target appliances in the residential buildings. In the proposed strategy, the parallel disaggregation network contains a long-term disaggregation network and a short-term disaggregation network, which can automatically and respectively learn the long-term trend features and short-term dynamic characteristics of the electrical appliances. This parallel structure can make full use of the advantages of different methods in feature extraction, so as to model the appliance features more comprehensively. To better extract the long-term and short-term features, in the long-term disaggregation subnet, we propose the double branch bi-directional temporal convolution network (DBB-TCN) which has a wider receptive field than the traditional temporal convolution networks (TCN), while in the short-term disaggregation subnet, we adopt the convolution auto-encoder to learn the short-term characteristics of the target appliances. Finally, detailed experiments and comparisons are made with two real-world datasets. Experimental results verified that the proposed parallel disaggregation method performs better than the existing methods under various evaluation criteria.
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Affiliation(s)
- Wenfeng Li
- Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China
| | - Xiaoping Deng
- Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China
| | - Ruiqi Wang
- State Grid Shandong Integrated Energy Services Co., Ltd., Jinan, China
| | - Songping Meng
- Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China
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Lu Q, Chen H, Zeng Y, Xue J, Cao X, Wang N, Wang Z. Intelligent facemask based on triboelectric nanogenerator for respiratory monitoring. NANO ENERGY 2022; 91:106612. [PMID: 34660183 PMCID: PMC8505024 DOI: 10.1016/j.nanoen.2021.106612] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/23/2021] [Accepted: 10/05/2021] [Indexed: 05/13/2023]
Abstract
The fast-spreading of novel coronavirus disease (COVID-19) has been sweeping around the globe and brought heavy casualties and economic losses, which creates dire needs for technological solutions into medical preventive actions. In this work, triboelectric nanogenerator for respiratory sensing (RS-TENG) has been designed and integrated with facemask, which endows the latter with respiratory monitoring function. The output of RS-TENG for respiratory flow can reach up to about 8 V and 0.8 μA respectively although it varies with different respiratory status, which proves the high sensitivity of RS-TENG for respiratory monitoring. An apnea alarm system can be constructed by combining the smart facemask with circuit modules so that timely alarm can be transmitted after people stop breathing. Furthermore, RS-TENG can be used to control household appliances, which brings convenience to the life of the disabled people. Considering its incomparable advantages such as small volume, easy fabrication, simple installation and economical applicability, such design is helpful for developing multifunctional health monitoring gadgets during the COVID-19 pandemic.
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Affiliation(s)
- Qixin Lu
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
| | - Hong Chen
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
| | - Yuanming Zeng
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiehui Xue
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
| | - Xia Cao
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Research Center for Bioengineering and Sensing Technology, Beijing Key Laboratory for Bioengineering and Sensing Technology, School of Chemistry and Biological engineering, and Beijing Municipal Key Laboratory of New Energy Materials and Technologies, University of Science and Technology Beijing, Beijing 100083, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ning Wang
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhonglin Wang
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Moshizi SA, Abedi A, Sanaeepur M, Pastras CJ, Han ZJ, Wu S, Asadnia M. Polymeric piezoresistive airflow sensor to monitor respiratory patterns. J R Soc Interface 2021; 18:20210753. [PMID: 34875876 PMCID: PMC8652268 DOI: 10.1098/rsif.2021.0753] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 12/29/2022] Open
Abstract
Monitoring human respiratory patterns is of great importance as it gives essential information for various medical conditions, e.g. sleep apnoea syndrome and chronic obstructive pulmonary disease and asthma, etc. Herein, we have developed a polymeric airflow sensor based on nanocomposites of vertically grown graphene nanosheets (VGNs) with polydimethylsiloxane (PDMS) and explored their applications in monitoring human respiration. The sensing performance of the VGNs/PDMS nanocomposite was characterized by exposing to a range of airflow rates (20-130 l min-1), and a linear performance with high sensitivity and low response time (mostly below 1 s) was observed. To evaluate the experimental results, finite-element simulation models were developed in the COMSOL Multiphysics package. The piezoresistive properties of VGNs/PDMS thin film and fluid-solid interaction were thoroughly studied. Laser Doppler vibrometry measures of sensor tip displacement closely approximated simulated deflection results and validated the dynamic response of the sensor. By comparing the proposed sensor and some other airflow sensors in the literature, it is concluded that the VGNs/PDMS airflow sensor has excellent features in terms of sensor height, detection range and sensitivity. The potential application of the VGNs/PDMS airflow sensor in detecting the respiration pattern of human exercises like walking, jogging and running has been demonstrated.
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Affiliation(s)
| | - Abolfazl Abedi
- Department of Electrical Engineering, Shahid Beheshti University, Tehran 19834, Iran
| | - Majid Sanaeepur
- Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak 3815688349, Iran
| | | | - Zhao Jun Han
- CSIRO Manufacturing, PO Box 218, 36 Bradfield Road, Lindfield, NSW 2070, Australia
| | - Shuying Wu
- School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
| | - Mohsen Asadnia
- School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
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10
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Issatayeva A, Beisenova A, Tosi D, Molardi C. Fiber-Optic Based Smart Textiles for Real-Time Monitoring of Breathing Rate. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3408. [PMID: 32560320 PMCID: PMC7348851 DOI: 10.3390/s20123408] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/20/2020] [Accepted: 05/25/2020] [Indexed: 11/17/2022]
Abstract
Wearable light textiles are gaining widespread interest in application for measurement and monitoring of biophysical parameters. Fiber optic sensors, in particular Bragg Grating (FBG) sensors, can be a competitive method for monitoring of respiratory behavior for chest and abdomen regions since the sensors are able to convert physical movement into wavelength shift. This study aims to show the performance of elastic belts with integrated optical fibers during the breathing activities done by two volunteers. Additionally, the work aims to determine how the positions of the volunteers affect the breathing pattern detected by optical fibers. As a reference, commercial mobile application for sensing vibration is used. The obtained results show that the FBGs are able to detect chest and abdomen movements during breathing and consequently reconstruct the breathing pattern. The accuracy of the results varies for two volunteers but remains consistent.
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Affiliation(s)
- Aizhan Issatayeva
- Department of Civil and Environmental Engineering, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
| | - Aidana Beisenova
- Department of Electrical and Computer Engineering, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (A.B.); (D.T.); (C.M.)
| | - Daniele Tosi
- Department of Electrical and Computer Engineering, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (A.B.); (D.T.); (C.M.)
- Laboratory of Biosensors and Bioinstruments, National Laboratory Astana, Nur-Sultan 010000, Kazakhstan
| | - Carlo Molardi
- Department of Electrical and Computer Engineering, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (A.B.); (D.T.); (C.M.)
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