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Signore MA, Rescio G, Francioso L, Casino F, Leone A. Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:2071. [PMID: 38610281 PMCID: PMC11014281 DOI: 10.3390/s24072071] [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: 02/28/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15-30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.
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Affiliation(s)
| | - Gabriele Rescio
- The National Research Council, Institute for Microelectronics and Microsystems (CNR IMM), Via Monteroni, 73100 Lecce, Italy; (M.A.S.); (L.F.); (F.C.); (A.L.)
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2
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Zhang T, Zhu J, Wang Q, Xie M, Meng K, Mao L, Yang L, Pan T, Gao M, Yao G, Lin Y. Flexible Antibacterial Respiratory Monitoring Sensor Based on Controllable Au-Modified Surface of Highly {001} Preferred Anatase Titanium Dioxide Thin Film. ACS Biomater Sci Eng 2024; 10:1722-1733. [PMID: 38373308 DOI: 10.1021/acsbiomaterials.3c01164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Respiratory signals are critical clinical diagnostic criteria for respiratory diseases and health conditions, and respiratory sensors play a crucial role in achieving the desired respiratory monitoring effect. High sensitivity to a single factor can improve the reliability of respiratory monitoring, and maintaining the hygiene of the sensors is also important for daily health monitoring. Herein, we propose a flexible Au-modified anatase titanium dioxide resistive respiratory sensor, which can be mechanically compliantly attached to curved surfaces for respiratory monitoring in different modalities (i.e., respiratory intensity, frequency, and rate). The uniform and preferentially oriented anatase titanium dioxide films gained by the polymer-assisted deposition technique can be fabricated on flexible substrates through a liquid-assisted transferring process. The Au modification can enhance surface plasmon resonance to facilitate the photocatalytic activity of titanium dioxide, and the optimized distribution of Au on the surface of titanium dioxide film made the sensor have an excellent antibacterial effect. The uniquely designed encapsulation can effectively control the contact between the surface of titanium dioxide films and electrodes, allowing the flexible sensor to exhibit fast response time (0.71 s) and recovery time (1.06 s) to respiratory as well as insensitivity or low sensitivity to other factors (i.e., gas composition, humidity, temperature, stress, and strain). This work provided an effective strategy for flexible wearable respiratory sensors and has great potential in daily respiratory monitoring for health management and pandemic control.
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Affiliation(s)
- Tianyao Zhang
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Jia Zhu
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qian Wang
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Maowen Xie
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ke Meng
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Longbiao Mao
- Department of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Li Yang
- Department of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Taisong Pan
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Guang Yao
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Cooperation on Applied Medicine Research Center, University of Electronics Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- School of Material and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Cooperation on Applied Medicine Research Center, University of Electronics Science and Technology of China, Chengdu 610054, China
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Vitazkova D, Foltan E, Kosnacova H, Micjan M, Donoval M, Kuzma A, Kopani M, Vavrinsky E. Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies. BIOSENSORS 2024; 14:90. [PMID: 38392009 PMCID: PMC10886711 DOI: 10.3390/bios14020090] [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: 01/02/2024] [Revised: 01/28/2024] [Accepted: 02/03/2024] [Indexed: 02/24/2024]
Abstract
This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.
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Affiliation(s)
- Diana Vitazkova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Erik Foltan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Helena Kosnacova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia
| | - Michal Micjan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Anton Kuzma
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Kopani
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
| | - Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
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Zhang Z, Kan EC. Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition. IEEE SENSORS JOURNAL 2023; 23:20116-20128. [PMID: 38510062 PMCID: PMC10950291 DOI: 10.1109/jsen.2023.3294329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.
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Affiliation(s)
- Zijing Zhang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Edwin C Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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Conroy TB, Araos J, Kan EC. Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38082805 DOI: 10.1109/embc40787.2023.10340193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.
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Li J, Quintin E, Wang H, McDonald BE, Farrell TR, Huang X, Clancy EA. Application-Layer Time Synchronization and Data Alignment Method for Multichannel Biosignal Sensors Using BLE Protocol. SENSORS (BASEL, SWITZERLAND) 2023; 23:3954. [PMID: 37112294 PMCID: PMC10144216 DOI: 10.3390/s23083954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/11/2023] [Indexed: 06/19/2023]
Abstract
Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved problem. Current solutions use custom wireless protocols or extra hardware for synchronization, forming custom systems with high power consumption that prohibit migration between commercial microcontrollers. We aimed to develop a better solution. We successfully developed a low-latency, Bluetooth low energy (BLE)-based data alignment method, implemented in the BLE application layer, making it transferable between manufacturer devices. The time synchronization method was tested on two commercial BLE platforms by inputting common sinusoidal input signals (over a range of frequencies) to evaluate time alignment performance between two independent peripheral nodes. Our best time synchronization and data alignment method achieved absolute time differences of 69 ± 71 μs for a Texas Instruments (TI) platform and 477 ± 490 μs for a Nordic platform. Their 95th percentile absolute errors were more comparable-under 1.8 ms for each. Our method is transferable between commercial microcontrollers and is sufficient for many biomedical applications.
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Affiliation(s)
- Jianan Li
- Worcester Polytechnic Institute, Worcester, MA 01609, USA; (J.L.); (H.W.); (X.H.)
| | - Eric Quintin
- Liberating Technologies, Inc., Holliston, MA 01746, USA; (E.Q.); (B.E.M.); (T.R.F.)
| | - He Wang
- Worcester Polytechnic Institute, Worcester, MA 01609, USA; (J.L.); (H.W.); (X.H.)
| | - Benjamin E. McDonald
- Liberating Technologies, Inc., Holliston, MA 01746, USA; (E.Q.); (B.E.M.); (T.R.F.)
| | - Todd R. Farrell
- Liberating Technologies, Inc., Holliston, MA 01746, USA; (E.Q.); (B.E.M.); (T.R.F.)
| | - Xinming Huang
- Worcester Polytechnic Institute, Worcester, MA 01609, USA; (J.L.); (H.W.); (X.H.)
| | - Edward A. Clancy
- Worcester Polytechnic Institute, Worcester, MA 01609, USA; (J.L.); (H.W.); (X.H.)
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Zhang Z, Conroy TB, Krieger AC, Kan EC. Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors. IEEE Trans Biomed Eng 2023; 70:1208-1218. [PMID: 37815956 DOI: 10.1109/tbme.2022.3212619] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
OBJECTIVE Respiratory disturbances during sleep are a prevalent health condition that affects a large adult population. The gold standard to evaluate sleep disorders including apnea is overnight polysomnography, which requires a trained technician for live monitoring and post-processing scoring. Currently, the disorder events can hardly be predicted using the respiratory waveforms preceding the events. The objective of this paper is to develop an autonomous system to detect and predict respiratory events reliably based on real-time covert sensing. METHODS A bed-integrated radio-frequency (RF) sensor by near-field coherent sensing (NCS) was employed to retrieve continuous respiratory waveforms without user's awareness. Overnight recordings were collected from 27 patients in the Weill Cornell Center for Sleep Medicine. We extracted respiratory features to feed into the random-forest machine learning model for disorder detection and prediction. The technician annotation, derived from observation by polysomnography, was used as the ground truth during the supervised learning. RESULTS Apneic event detection achieved a sensitivity and specificity up to 88.6% and 89.0% for k-fold validation, and 83.1% and 91.6% for subject-independent validation. Prediction of forthcoming apneic events could be made up to 90 s in advance. Apneic event prediction achieved a sensitivity and specificity up to 81.3% and 82.1% for k-fold validation, and 80.5% and 82.4% for subject-independent validation. The most important features for event detection and prediction can be assessed in the learning model. CONCLUSION A bed-integrated RF sensor can covertly and reliably detect and predict apneic events. SIGNIFICANCE Predictive warning of the sleep disorders in advance can intervene serious apnea, especially for infants, servicemen, and patients with chronic conditions.
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In-ear infrasonic hemodynography with a digital health device for cardiovascular monitoring using the human audiome. NPJ Digit Med 2022; 5:189. [PMID: 36550288 PMCID: PMC9780339 DOI: 10.1038/s41746-022-00725-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual's unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.
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Sharma P, Zhang Z, Conroy TB, Hui X, Kan EC. Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:8047. [PMID: 36298396 PMCID: PMC9610852 DOI: 10.3390/s22208047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
This work presents a study on users' attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user's baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively.
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Wang H, Gao X, Shi Y, Wu D, Li C, Wang W. Effects of trunk posture on cardiovascular and autonomic nervous systems: A pilot study. Front Physiol 2022; 13:1009806. [PMID: 36330208 PMCID: PMC9623330 DOI: 10.3389/fphys.2022.1009806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/28/2022] [Indexed: 01/28/2024] Open
Abstract
Objective: Although regular and moderate physical activity has been shown to improve the cardiovascular and autonomic nervous systems, little has been done to study the effects of postural changes in the movement on the heart and autonomic nervous system. To uncover changes in cardiac function and autonomic nerves induced by different underlying posture transitions and explore which trunk postures lead to chronic sympathetic activation. Therefore, this study investigated the effects of trunk posture on the cardiovascular and autonomic nervous systems. Methods: Twelve male subjects (age 24.7 ± 1.3) underwent this study. The non-invasive cardiac output NICOM monitoring equipment and the FIRSTBEAT system are used to dynamically monitor seven trunk postures in the sitting position simultaneously (neutral position, posterior extension, forward flexion, left lateral flexion, right lateral flexion, left rotation, right rotation). Each posture was maintained for 3 min, and the interval between each movement was 3 min to ensure that each index returned to the baseline level. Repeated analysis of variance test was used to compare and analyze the differences in human cardiac function, heart rate variability index, and respiratory rate under different postures. Results: Compared with the related indicators of cardiac output in a neutral trunk position: the cardiac index (CI) was significantly reduced in forwarding flexion and left rotation (3.48 ± 0.34 vs. 3.21 ± 0.50; 3.48 ± 0.34 vs. 3.21 ± 0.46, Δ L/(min/m2)) (p = 0.016, p = 0.013), cardiac output decreased significantly (6.49 ± 0.78 vs. 5.93 ± 0.90; 6.49 ± 0.78 vs. 6.00 ± 0.96, Δ L/min) (p = 0.006, p = 0.014), the stroke volume (stroke volume)decreased significantly (87.90 ± 15.10 vs. 81.04 ± 16.35; 87.90 ± 15.10 vs. 79.24 ± 16.83, Δ ml/beat) (p = 0.017, p = 0.0003); heart rate increased significantly in posterior extension (75.08 ± 10.43 vs. 78.42 ± 10.18, Δ beat/min) (p = 0.001); left rotation stroke volume index (SVI) decreased significantly (47.28 ± 7.97 vs. 46.14 ± 8.06, Δ ml/m2) (p = 0.0003); in the analysis of HRV-related indicators, compared with the neutral trunk position, the LF/HF of the posterior extension was significantly increased (1.90 ± 1.38 vs. 3.00 ± 1.17, p = 0.037), and the LF/HF of the forward flexion was significantly increased (1.90 ± 1.38 vs. 2.85 ± 1.41, p = 0.041), and the frequency-domain index LF/HF of right rotation was significantly increased (1.90 ± 1.38 vs. 4.06 ± 2.19, p = 0.008). There was no significant difference in respiratory rate (p > 0.05). Conclusion: A neutral trunk is the best resting position, and deviations from a neutral trunk position can affect the cardiovascular and autonomic nervous systems, resulting in decreased stroke volume, increased heart rate, and relative activation of sympathetic tone.
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Affiliation(s)
- Hao Wang
- Sports Rehabilitation Research Center, China Institute of Sport Science, Beijing, China
| | - Xiaolin Gao
- Sports Rehabilitation Research Center, China Institute of Sport Science, Beijing, China
| | - Yongjin Shi
- Department of Sports and Arts, China Agricultural University, Beijing, China
| | - Dongzhe Wu
- Sports Rehabilitation Research Center, China Institute of Sport Science, Beijing, China
| | - Chuangtao Li
- Sports Rehabilitation Research Center, China Institute of Sport Science, Beijing, China
| | - Wendi Wang
- Sports Rehabilitation Research Center, China Institute of Sport Science, Beijing, China
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Soliman MM, Ganti VG, Inan OT. Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals. IEEE SENSORS JOURNAL 2022; 22:18093-18103. [PMID: 37091042 PMCID: PMC10120872 DOI: 10.1109/jsen.2022.3196601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.
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Affiliation(s)
- Moamen M Soliman
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Venu G Ganti
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332
| | - Omer T Inan
- School of Electrical and Computer Engineering and, by courtesy, the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
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Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device. DATA 2022. [DOI: 10.3390/data7090132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety Inventory and Hamilton Anxiety Rating Scale) were used to assess overall anxiety before induction. The data were collected at Simon Fraser University from participants aged 18–56, all of whom were healthy at the time. The ECG and RES signals were collected simultaneously while participants continuously watched video clips that stimulated anxiety-inducing (negative experience) and non-anxiety-inducing events (positive experience). The ECG and RES signals were recorded simultaneously at 500 Hz. The final dataset consisted of psychological scores and physiological signals from 19 participants (14 males and 5 females) who watched eight video clips. This dataset can be used to explore the instantaneous relationship between ECG and RES waveforms and anxiety-inducing video clips to uncover and evaluate the latent characteristic information contained in these biosignals.
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13
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Zang C, Zhang C, Zhang M, Niu Q. An RFID-Based Method for Multi-Person Respiratory Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:6166. [PMID: 36015926 PMCID: PMC9416178 DOI: 10.3390/s22166166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Respiratory monitoring is widely used in the field of health care. Traditional respiratory monitoring methods bring much inconvenience to users. In recent years, a great number of respiratory monitoring methods based on wireless technology have emerged, but multi-person respiratory monitoring is still very challenging; therefore, this paper explores multi-person respiratory monitoring. Firstly, the characteristics of human respiratory movement have been analyzed, and a suitable tag deployment method for respiratory monitoring is proposed. Secondly, aiming at the ambiguity and entanglement of radio frequency identification (RFID) phase data, a method of removal of phase ambiguity and phase wrapping is given. Then, in order to monitor multi-person respiration in a noisy environment, the frequency extraction method and waveform reconstruction method of multi-person respiration are proposed. Finally, the feasibility of the method is verified by experiments.
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Affiliation(s)
- Chaowei Zang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
- China Pingmei Shenma Group, Pingdingshan 467000, China
| | - Chi Zhang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Min Zhang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Qiang Niu
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
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Asiain D, Ponce de León J, Beltrán JR. MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:5775. [PMID: 35957330 PMCID: PMC9371105 DOI: 10.3390/s22155775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/22/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.
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Affiliation(s)
- David Asiain
- Department of Electronics, Escuela Universitaria Politécnica de La Almunia, La Almunia de Doña Godina, 50100 Zaragoza, Spain;
| | - Jesús Ponce de León
- Department of Electronics, Escuela Universitaria Politécnica de La Almunia, La Almunia de Doña Godina, 50100 Zaragoza, Spain;
| | - José Ramón Beltrán
- Department of Electronics Engineering and Communications, Escuela de Ingeniería y Arquitectura, I3A, Universidad de Zaragoza, 50018 Zaragoza, Spain;
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15
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Ates HC, Nguyen PQ, Gonzalez-Macia L, Morales-Narváez E, Güder F, Collins JJ, Dincer C. End-to-end design of wearable sensors. NATURE REVIEWS. MATERIALS 2022; 7:887-907. [PMID: 35910814 PMCID: PMC9306444 DOI: 10.1038/s41578-022-00460-x] [Citation(s) in RCA: 173] [Impact Index Per Article: 86.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 05/03/2023]
Abstract
Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.
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Affiliation(s)
- H. Ceren Ates
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | - Peter Q. Nguyen
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | | | - Eden Morales-Narváez
- Biophotonic Nanosensors Laboratory, Centro de Investigaciones en Óptica, León, Mexico
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, UK
| | - James J. Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
- Institute of Medical Engineering & Science, Department of Biological Engineering, MIT, Cambridge, MA USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Can Dincer
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
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Gwak M, Vatanparvar K, Kuang J, Gao A. Motion- Based Respiratory Rate Estimation with Motion Artifact Removal Using Video of Face and Upper Body. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1961-1967. [PMID: 36086435 DOI: 10.1109/embc48229.2022.9871231] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Respiratory rate (RR) is a significant indicator of health conditions. Remote contactless measurement of RR is gaining popularity with recent respiratory tract infection awareness. Among various methods of contactless RR measurement, a video of an individual can be used to obtain an instantaneous RR. In this paper, we introduce an RR estimation based on the subtle motion of the head or upper chest captured on an RGB camera. Motion-based respiratory monitoring allows us to acquire RR from individuals with partial face coverings, such as glasses or a face mask. However, motion-based RR estimation is vulnerable to the subject's voluntary movement. In this work, adaptive selection between face and chest regions plus a motion artifact removal technique enables us to obtain a much cleaner respiratory signal from the video recordings. The average mean absolute error (MAE) for controlled and natural breathing is 1.95 BPM using head motion only and 1.28 BPM using chest motion only. Our results demonstrate the possibility of continuous monitoring of breathing rate in real-time with any personal device equipped with an RGB camera, such as a laptop or a smartphone.
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17
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Conroy TB, Zhou J, Kan EC. Physiological Features of Cardiac Ventricle and Valve Dynamics from Wearable Radio-Frequency Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2906-2911. [PMID: 36086442 DOI: 10.1109/embc48229.2022.9871038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Early detection of cardiovascular diseases via non-invasive, convenient, and continuous monitoring is crucial to reducing preventable deaths. This paper illustrates such monitoring using wearable near-field radio-frequency sensors to analyze ventricle and valve transients, which can be used as indicators of myriad cardiac disorders. We applied a novel vector injection signal processing method to improve timing consistency in ventricular contraction, ventricular relaxation, and valve opening extraction. The median relative timing error in valve opening detection was 14.7ms and 37.8ms for semilunar and atrioventricular valves, respectively, as benchmarked by the S1 and S2 heart sounds from a synchronous phonocardiogram. Clinical Relevance- No wearable sensor currently exists to conveniently and reliably evaluate ventricular and valvular dynamics, specifically valvular opening. Beyond extraction of the heart rate and its variation, the method in this paper has the potential to enable non-invasive measurements of detailed cardiac cycle timing features including valve openings, isovolumetric contraction/relaxation times, and ejection periods, improving the monitoring of patient health away from clinical healthcare centers.
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18
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Vasquez BA, Betriana F, Nemenzo E, Inabangan AK, Tanioka R, Garcia L, Juntasopeepun P, Tanioka T, Locsin RC. Effects of Healthcare Technologies on the Promotion of Physical Activities in Older Persons: A Systematic Review. Inform Health Soc Care 2022; 48:196-210. [PMID: 35699246 DOI: 10.1080/17538157.2022.2086874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
Abstract
This study aimed to explore the effects of health technologies on the promotion of health through physical activities of older persons. Following PRISMA guidelines, a systematic review of relevant articles published prior to 2020 was conducted from selected indices such as COCHRANE, PubMed, Science Direct, Proquest, including the use of hand search procedure. Twenty-seven articles were analyzed with significant findings influential to older people nursing: types of health technologies used for promoting physical activity; effects of technology use in promoting physical activity of older person care; and aspects that need to be considered in technology use among older persons. Characteristics of technologies were accuracy, usefulness, reliability, comfort, safety, and relevancy. Most technologies promoting physical activities for older people were wearable technologies that use artificial intelligence. Altogether, these technologies influenced overall healthcare behaviors of older persons. With healthcare technology efficiencies, proficiencies, and dependencies, technology-based healthcare have served older people well. Most technologies for older people care, such as wearables, reliably produce characteristics enhancing dependency and accuracy of bio-behavioral information influencing physical activities of older persons. Health technologies foster the values of physical activities among older persons thereby promoting healthy living.
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Affiliation(s)
- Brian A Vasquez
- Majmaah University, College of Applied Medical Sciences, Majmaah, Kingdom of Saudi Arabia
| | - Feni Betriana
- Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Endrex Nemenzo
- College of Nursing, Cebu Normal University, Cebu City, Philippines.,Minghsin University of Science and Technology, Hsinchu, Taiwan
| | | | - Ryuichi Tanioka
- Department of Rehabilitation, Hiroshima Cosmopolitan University, Hiroshima, Japan
| | - Laurence Garcia
- College of Nursing and Health Sciences, Cebu Normal University, Cebu City, Philippines
| | | | - Tetsuya Tanioka
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Rozzano C Locsin
- Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.,Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan.,Florida Atlantic University, Boca Raton, Florida, USA
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Ye Z, Ling Y, Yang M, Xu Y, Zhu L, Yan Z, Chen PY. A Breathable, Reusable, and Zero-Power Smart Face Mask for Wireless Cough and Mask-Wearing Monitoring. ACS NANO 2022; 16:5874-5884. [PMID: 35298138 DOI: 10.1021/acsnano.1c11041] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We herein introduce a lightweight and zero-power smart face mask, capable of wirelessly monitoring coughs in real time and identifying proper mask wearing in public places during a pandemic. The smart face mask relies on the compact, battery-free radio frequency (RF) harmonic transponder, which is attached to the inner layer of the mask for detecting its separation from the face. Specifically, the RF transponder composed of miniature antennas and passive frequency multiplier is made of spray-printed silver nanowires (AgNWs) coated with a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) passivation layer and the recently discovered multiscale porous polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) substrate. Unlike conventional on-chip or on-board wireless sensors, the SEBS-AgNWs/PEDOT:PSS-based RF transponder is lightweight, stretchable, breathable, and comfortable. In addition, this wireless device has excellent resilience and robustness in long-term and repeated usages (i.e., repeated placement and removal of the soft transponder on the mask). We foresee that this wireless smart face mask, providing simultaneous cough and mask-wearing monitoring, may mitigate virus-transmissive events by tracking the potential contagious person and identifying mask-wearing conditions. Moreover, the ability to wirelessly assess cough frequencies may improve diagnosis accuracy for dealing with several diseases, such as chronic obstructive pulmonary disease.
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Affiliation(s)
- Zhilu Ye
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Yun Ling
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Minye Yang
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Yadong Xu
- Department of Biomedical, Biological, and Chemical Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Liang Zhu
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Zheng Yan
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
- Department of Biomedical, Biological, and Chemical Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Pai-Yen Chen
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
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20
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Yang M, Ye Z, Alsaab N, Farhat M, Chen PY. In-Vitro Demonstration of Ultra-Reliable, Wireless and Batteryless Implanted Intracranial Sensors Operated on Loci of Exceptional Points. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:287-295. [PMID: 35380967 DOI: 10.1109/tbcas.2022.3164697] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Vital signal monitoring, such as pulse, respiration rate, intra-organ and intra-vascular pressure, can provide important information for determination of clinic diagnosis, treatments, and surgical protocols. Nowadays, micromachined bioimplants, equipped with antennas for converting bio-signals to modulated radio transmissions, may allow remote continuous monitoring of patients' vital signs. Yet, current passive biotelemetry techniques usually suffer from poor signal reproducibility and robustness in light of inevitable misalignment between transmitting and receiving antennas. Here, we seek to address this long-existing challenge and to robustly acquire information from a passive wireless intracranial pressure (or brain pressure) sensor by introducing a novel, high-performance biotelemetry system. In spite of variable inductive links, this biotelemetry system may have absolute accuracy by leveraging the uniqueness of loci of exceptional points (EPs) in non-Hermitian radio-frequency (RF) electronic systems with parity-time (PT) symmetry. Our in-vitro experimental demonstration shows that the proposed intracranial (ICP) monitoring system can provide a sub-mmHg resolution in the ICP range of 0-20 mmHg and ultra-robust wireless data acquisition against the misalignment-induced weakening of inductive link. Our results could provide a practical pathway toward reliable, real-time wireless monitoring of ICP, and other vital signals generated by bio-implants and wearables.
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21
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Qiu C, Wu F, Han W, Yuce MR. A Wearable Bioimpedance Chest Patch for Real-Time Ambulatory Respiratory Monitoring. IEEE Trans Biomed Eng 2022; 69:2970-2981. [PMID: 35275808 DOI: 10.1109/tbme.2022.3158544] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring. METHODS A wearable chest-worn patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission. RESULTS The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% to the ground truth, respectively. Additionally, the devices performance is evaluated in real-world scenarios both indoors and outdoors. CONCLUSION The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions. SIGNIFICANCE This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life.
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22
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Respiratory Monitoring by Ultrafast Humidity Sensors with Nanomaterials: A Review. SENSORS 2022; 22:s22031251. [PMID: 35161997 PMCID: PMC8838830 DOI: 10.3390/s22031251] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 02/01/2023]
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 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.
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Parchani G, Kumar G, Rao R, Udupa K, Saran V. Efficacy of Non-contact BallistocardiographySystem to Determine Heart Rate Variability. Ann Neurosci 2022; 29:16-20. [PMID: 35875429 PMCID: PMC9305910 DOI: 10.1177/09727531211063426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 11/01/2021] [Indexed: 01/01/2023] Open
Abstract
Background: Functions of the autonomic nervous system have cardinal importance in day-to-day life. Heart rate variability (HRV) has been shown to estimate the functioning of the autonomic nervous system. Imbalance in the functioning of the autonomic nervous system is seen to be associated with chronic conditions such as chronic kidney disease, cardiovascular diseases, diabetes mellitus, and so on. Purpose: To evaluate the efficacy of a non-contact ballistocardiography (BCG) system to calculate HRV parameters by comparing them to the parameters derived from a standard commercial software that uses an electrocardiogram (ECG). Methods: Current study captured an ECG signal using a three-channel ECG Holter machine, whereas the BCG signal was captured using a BCG sensor sheet consisting of vibroacoustic sensors placed under the mattress of the participants of the study. Results: The study was conducted on 24 subjects for a total of 54 overnight recordings. The proposed method covered 97.92% epochs of the standard deviation of NN intervals (SDNN) and 99.27% epochs of root mean square of successive differences (RMSSD) within 20 ms and 30 ms tolerance, respectively, whereas 98.84% of two-min intervals for low-frequency (LF) to high-frequency (HF) ratio was covered within a tolerance of 1. Kendall’s coefficient of concordance was also calculated, giving a P < .001 for all the three parameters and coefficients 0.66, 0.55, and 0.44 for SDNN, RMSSD, and LF/HF, respectively. Conclusion: The results show that HRV parameters captured using unobtrusive and non-invasive BCG sensors are comparable to HRV calculated using ECG.
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Affiliation(s)
- Gaurav Parchani
- Turtle Shell Technologies Pvt. Ltd, Bengaluru, Karnataka, India
| | - Gulshan Kumar
- Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Raghavendra Rao
- Department of Research, Central Council of Research in Yoga and Naturopathy, New Delhi, Delhi, India
| | - Kaviraja Udupa
- Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Vibhor Saran
- Turtle Shell Technologies Pvt. Ltd, Bengaluru, Karnataka, India
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Jung H, Kimball JP, Receveur T, Gazi AH, Agdeppa ED, Inan OT. Estimation of Tidal Volume Using Load Cells on a Hospital Bed. IEEE J Biomed Health Inform 2022; 26:3330-3341. [PMID: 34995200 DOI: 10.1109/jbhi.2022.3141209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifi- able respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respi- ratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respira- tory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low- frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.
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25
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Zhang Z, Sharma P, Conroy TB, Phongtankuel V, Kan EC. Objective Scoring of Physiologically Induced Dyspnea by Non-Invasive RF Sensors. IEEE Trans Biomed Eng 2022; 69:432-442. [PMID: 34255624 PMCID: PMC8743005 DOI: 10.1109/tbme.2021.3096462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Dyspnea, also known as the patient's feeling of difficult or labored breathing, is one of the most common symptoms for respiratory disorders. Dyspnea is usually self-reported by patients using, for example, the Borg scale from 0 - 10, which is however subjective and problematic for those who refuse to cooperate or cannot communicate. The objective of this paper was to develop a learning-based model that can evaluate the correlation between the self-report Borg score and the respiratory metrics for dyspnea induced by exertion and increased airway resistance. METHODS A non-invasive wearable radio-frequency sensor by near-field coherent sensing was employed to retrieve continuous respiratory data with user comfort and convenience. Self-report dyspnea scores and respiratory features were collected on 32 healthy participants going through various physical and breathing exercises. A machine learning model based on the decision tree and random forest then produced an objective dyspnea score. RESULTS For unseen data as well as unseen participants, the objective dyspnea score can be in reasonable agreement with the self-report score, and the importance factor of each respiratory metrics can be assessed. CONCLUSION An objective dyspnea score can potentially complement or substitute the self-report for physiologically induced dyspnea. SIGNIFICANCE The method can potentially formulate a baseline for clinical dyspnea assessment and help caregivers track dyspnea continuously, especially for patients who cannot report themselves.
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Affiliation(s)
- Zijing Zhang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Pragya Sharma
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Thomas Bradley Conroy
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Veerawat Phongtankuel
- Geriatrics and Palliative Medicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Edwin C. Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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26
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Mirjalali S, Peng S, Fang Z, Wang C, Wu S. Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of Covid-19. ADVANCED MATERIALS TECHNOLOGIES 2022; 7:2100545. [PMID: 34901382 PMCID: PMC8646515 DOI: 10.1002/admt.202100545] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 07/22/2021] [Indexed: 05/11/2023]
Abstract
Wearable sensors are emerging as a new technology to detect physiological and biochemical markers for remote health monitoring. By measuring vital signs such as respiratory rate, body temperature, and blood oxygen level, wearable sensors offer tremendous potential for the noninvasive and early diagnosis of numerous diseases such as Covid-19. Over the past decade, significant progress has been made to develop wearable sensors with high sensitivity, accuracy, flexibility, and stretchability, bringing to reality a new paradigm of remote health monitoring. In this review paper, the latest advances in wearable sensor systems that can measure vital signs at an accuracy level matching those of point-of-care tests are presented. In particular, the focus of this review is placed on wearable sensors for measuring respiratory behavior, body temperature, and blood oxygen level, which are identified as the critical signals for diagnosing and monitoring Covid-19. Various designs based on different materials and working mechanisms are summarized. This review is concluded by identifying the remaining challenges and future opportunities for this emerging field.
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Affiliation(s)
- Sheyda Mirjalali
- School of EngineeringMacquarie University SydneySydneyNSW2109Australia
| | - Shuhua Peng
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
| | | | - Chun‐Hui Wang
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
| | - Shuying Wu
- School of EngineeringMacquarie University SydneySydneyNSW2109Australia
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
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27
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Ruiz LJL, Zhu J, Fitzgerald L, Quinn D, Lach J. Capacitive Sensing for Monitoring Stent Patency in the Central Airway. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5441-5445. [PMID: 34892357 DOI: 10.1109/embc46164.2021.9630965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Central airway obstruction (CAO) is a respiratory disorder characterized by the blockage of the trachea and/or the main bronchi that can be life-threatening. Airway stenting is a palliative procedure for CAO commonly used given its efficacy. However, mucus impaction, secretion retention, and granulation tissue growth are known complications that can counteract the stent's benefits. To prevent these situations, patients are routinely brought into the hospital to check stent patency, incurring a burden for the patient and the health care system, unnecessarily when no problems are found. In this paper, we introduce a capacitive sensor embedded in a stent that can detect solid and colloidal obstructions in the stent, as such obstructions alter the capacitor's dielectric relative permittivity. In the case of colloidal obstructions (e.g., mucus), volumes as low as 0.1 ml can be detected. Given the small form factor of the sensor, it could be adapted to a variety of stent types without changing the standard bronchoscopy insertion method. The proposed system is a step forward in the development of smart airway stents that overcome the limitations of current stenting technology.Clinical Relevance- This establishes the foundation for smart stent technology to monitor stent patency as an alternative to rutinary bronchoscopies.
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Batista E, Moncusi MA, López-Aguilar P, Martínez-Ballesté A, Solanas A. Sensors for Context-Aware Smart Healthcare: A Security Perspective. SENSORS (BASEL, SWITZERLAND) 2021; 21:6886. [PMID: 34696099 PMCID: PMC8537585 DOI: 10.3390/s21206886] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.
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Affiliation(s)
- Edgar Batista
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
- SIMPPLE S.L., C. Joan Maragall 1A, 43003 Tarragona, Spain
| | - M. Angels Moncusi
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Pablo López-Aguilar
- Anti-Phishing Working Group EU, Av. Diagonal 621–629, 08028 Barcelona, Spain;
| | - Antoni Martínez-Ballesté
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Agusti Solanas
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
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Hui X, Zhou J, Sharma P, Conroy TB, Zhang Z, Kan EC. Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:756-764. [PMID: 34310320 DOI: 10.1109/tbcas.2021.3099865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.
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Al-Halhouli A, Albagdady A, Alawadi J, Abeeleh MA. Monitoring Symptoms of Infectious Diseases: Perspectives for Printed Wearable Sensors. MICROMACHINES 2021; 12:620. [PMID: 34072174 PMCID: PMC8229808 DOI: 10.3390/mi12060620] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/21/2021] [Accepted: 05/23/2021] [Indexed: 12/23/2022]
Abstract
Infectious diseases possess a serious threat to the world's population, economies, and healthcare systems. In this review, we cover the infectious diseases that are most likely to cause a pandemic according to the WHO (World Health Organization). The list includes COVID-19, Crimean-Congo Hemorrhagic Fever (CCHF), Ebola Virus Disease (EBOV), Marburg Virus Disease (MARV), Lassa Hemorrhagic Fever (LHF), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), Nipah Virus diseases (NiV), and Rift Valley fever (RVF). This review also investigates research trends in infectious diseases by analyzing published research history on each disease from 2000-2020 in PubMed. A comprehensive review of sensor printing methods including flexographic printing, gravure printing, inkjet printing, and screen printing is conducted to provide guidelines for the best method depending on the printing scale, resolution, design modification ability, and other requirements. Printed sensors for respiratory rate, heart rate, oxygen saturation, body temperature, and blood pressure are reviewed for the possibility of being used for disease symptom monitoring. Printed wearable sensors are of great potential for continuous monitoring of vital signs in patients and the quarantined as tools for epidemiological screening.
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Affiliation(s)
- Ala’aldeen Al-Halhouli
- NanoLab/Mechatronics Engineering Department, School of Applied Technical Sciences, German Jordanian University (GJU), Amman 11180, Jordan; (A.A.); (J.A.)
- Institute of Microtechnology, Technische Universität Braunschweig, 38124 Braunschweig, Germany
- Faculty of Engineering, Middle East University, Amman 11831, Jordan
| | - Ahmed Albagdady
- NanoLab/Mechatronics Engineering Department, School of Applied Technical Sciences, German Jordanian University (GJU), Amman 11180, Jordan; (A.A.); (J.A.)
| | - Ja’far Alawadi
- NanoLab/Mechatronics Engineering Department, School of Applied Technical Sciences, German Jordanian University (GJU), Amman 11180, Jordan; (A.A.); (J.A.)
| | - Mahmoud Abu Abeeleh
- Department of Surgery, Faculty of Medicine, The University of Jordan, Amman 11942, Jordan;
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Requirements for Supporting Diagnostic Equipment of Respiration Process in Humans. SENSORS 2021; 21:s21103479. [PMID: 34067611 PMCID: PMC8156866 DOI: 10.3390/s21103479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/30/2021] [Accepted: 05/13/2021] [Indexed: 11/24/2022]
Abstract
There is abundant worldwide research conducted on the subject of the methods of human respiration process examination. However, many of these studies describe methods and present the results while often lacking insight into the hardware and software aspects of the devices used during the research. This paper’s goal is to present new equipment for assessing the parameters of human respiration, which can be easily adopted for daily diagnosis. This work deals with the issue of developing the correct method of obtaining measurement data. The requirements of the acquisition parameters are clearly pointed out and examples of the medical applications of the described device are shown. Statistical analysis of acquired signals proving its usability is also presented. In the examples of selected diseases of the Upper Respiratory Tract (URT), the advantages of the developed apparatus for supporting the diagnosis of URT patency have been proven.
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Zhang Z, Sharma P, Zhou J, Hui X, Kan EC. Furniture-Integrated Respiration Sensors by Notched Transmission Lines. IEEE SENSORS JOURNAL 2021; 21:5303-5311. [PMID: 33746625 PMCID: PMC7978236 DOI: 10.1109/jsen.2020.3028970] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Non-invasive respiration sensors integrated into furniture can be invisible to the user and greatly enhance comfort and convenience to facilitate many applications. Current sensors often require user cooperation or fitting, which discourages frequent usage. We present a new respiration sensor integrated into a bed or a chair by modifying a radio-frequency (RF) coaxial cable structure with a designed notch. The lung motion is coupled to the electromagnetic leakage at the notch through near-field coherent sensing (NCS). The sensors, covered with fabrics and positioned under the abdomen and thorax, can capture the respiratory waveforms and derive the breath rate. The heart rate can also be evaluated in the same setup with proper filtering. The sensor design can tolerate large position variation to accommodate user uncertainties. Various voluntary exercises of normal, deep, fast, held and blocked breathing were measured under different postures of supine, recumbent and sitting by the carrier frequency range between 900MHz and 2.4GHz. The breath rate from 10 participants compare well with the synchronous commercial chest-belt sensors in all breathing routines.
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Affiliation(s)
- Zijing Zhang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Pragya Sharma
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Jianlin Zhou
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Xiaonan Hui
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Edwin C Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor. SENSORS 2021; 21:s21020468. [PMID: 33440773 PMCID: PMC7826615 DOI: 10.3390/s21020468] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 12/23/2022]
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 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.
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Tipparaju VV, Wang D, Yu J, Chen F, Tsow F, Forzani E, Tao N, Xian X. Respiration pattern recognition by wearable mask device. Biosens Bioelectron 2020; 169:112590. [PMID: 32927349 PMCID: PMC7572779 DOI: 10.1016/j.bios.2020.112590] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/22/2020] [Accepted: 09/01/2020] [Indexed: 11/28/2022]
Abstract
Compared to heart rate, body temperature and blood pressure, respiratory rate is the vital sign that has been often overlooked, largely due to the lack of easily accessible tool for reliable and natural respiration monitoring. To address this unmet need, we designed and built a wearable, stand-alone, fully integrated mask device for accurate tracking of respiration in free-living conditions. The wearable mask device can provide comprehensive respiration information in a wearable and wireless manner. It can not only accurately measure respiratory rate, tidal volume, respiratory minute volume, and peak flow rate but also recognize unique respiration pattern of the subject via Principle Component Analysis (PCA) algorithms. The reported wearable mask device and respiratory pattern recognition algorithms could be widely used in routine clinical examination, lung function assessment, asthma and chronic obstructive pulmonary disease (COPD) management, metabolic rate measurement, capnography, spirometry, sleep pattern analysis, and biometrics.
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Affiliation(s)
- Vishal Varun Tipparaju
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Di Wang
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Jingjing Yu
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Fang Chen
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Francis Tsow
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Erica Forzani
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Nongjian Tao
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Xiaojun Xian
- Center for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
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