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Chaturvedi V, Falk M, Björklund S, Gonzalez-Martinez JF, Shleev S. Monoolein-Based Wireless Capacitive Sensor for Probing Skin Hydration. SENSORS (BASEL, SWITZERLAND) 2024; 24:4449. [PMID: 39065849 PMCID: PMC11280606 DOI: 10.3390/s24144449] [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: 05/24/2024] [Revised: 07/06/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024]
Abstract
Capacitive humidity sensors typically consist of interdigitated electrodes coated with a dielectric layer sensitive to varying relative humidity levels. Previous studies have investigated different polymeric materials that exhibit changes in conductivity in response to water vapor to design capacitive humidity sensors. However, lipid films like monoolein have not yet been integrated with humidity sensors, nor has the potential use of capacitive sensors for skin hydration measurements been fully explored. This study explores the application of monoolein-coated wireless capacitive sensors for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein-water system. This sensitivity hinges on the water absorption and release from the surrounding environment. Tested across various humidity levels and temperatures, these novel double functional sensors feature interdigitated electrodes covered with monoolein and show promising potential for wireless detection of skin hydration. The water uptake and rheological behavior of monoolein in response to humidity were evaluated using a quartz crystal microbalance with dissipation monitoring. The findings from these experiments suggest that the capacitance of the system is primarily influenced by the amount of water in the monoolein system, with the lyotropic or physical state of monoolein playing a secondary role. A proof-of-principle demonstration compared the sensor's performance under varying conditions to that of other commercially available skin hydration meters, affirming its effectiveness, reliability, and commercial viability.
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Affiliation(s)
- Vivek Chaturvedi
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden; (V.C.); (S.B.); (J.F.G.-M.)
- Biofilms Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden
| | - Magnus Falk
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden; (V.C.); (S.B.); (J.F.G.-M.)
- Biofilms Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden
| | - Sebastian Björklund
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden; (V.C.); (S.B.); (J.F.G.-M.)
- Biofilms Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden
| | - Juan F. Gonzalez-Martinez
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden; (V.C.); (S.B.); (J.F.G.-M.)
- Department of Applied Physics and Naval Technology, Polytechnical University of Cartagena, 30202 Cartagena, Spain
| | - Sergey Shleev
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden; (V.C.); (S.B.); (J.F.G.-M.)
- Biofilms Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden
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2
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Baron R, Haick H. Mobile Diagnostic Clinics. ACS Sens 2024; 9:2777-2792. [PMID: 38775426 PMCID: PMC11217950 DOI: 10.1021/acssensors.4c00636] [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] [Received: 03/20/2024] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 06/29/2024]
Abstract
This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.
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Affiliation(s)
- Roni Baron
- Department
of Biomedical Engineering, Technion—Israel
Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department
of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion—Israel Institute of Technology, Haifa 3200003, Israel
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3
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Saied I, Alzaabi A, Arslan T. Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:2233. [PMID: 38610446 PMCID: PMC11014059 DOI: 10.3390/s24072233] [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: 02/22/2024] [Revised: 03/21/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects' breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath's water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another.
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Affiliation(s)
- Imran Saied
- Advanced Care Research Centre, The University of Edinburgh, Edinburgh EH9 3JW, UK;
| | - Aaesha Alzaabi
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3JW, UK;
| | - Tughrul Arslan
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3JW, UK;
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4
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Matti C, Essig S, Föhn Z, Balthasar A. The Role of Wearable Sensors in the Future Primary Healthcare - Preferences of the Adult Swiss Population: A Mixed Methods Approach. J Med Syst 2023; 47:111. [PMID: 37907653 PMCID: PMC10618354 DOI: 10.1007/s10916-023-01998-1] [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] [Received: 11/08/2022] [Accepted: 09/28/2023] [Indexed: 11/02/2023]
Abstract
Wearable sensors have the potential to increase continuity of care and reduce healthcare expenditure. The user concerns and preferences regarding wearable sensors are the least addressed topic in related literature. Therefore, this study aimed first, to examine the preferences of the adult Swiss population regarding the use of wearable sensors in primary healthcare. Second, the study aimed to explain and learn more about these preferences and why such wearable sensors would or would not be used. An explanatory sequential design was used to reach the two aims. In the initial quantitative phase preferences of a nationwide survey were analyzed descriptively and a multivariable ordered logistic regression was used to identify key characteristics, that influence the preferences. In the second phase, eight semi-structured interviews were conducted. The cleaned study sample of the survey included 687 participants, 46% of whom gave a positive rating regarding the use of wearable sensors. In contrast, 44% gave a negative rating and 10% were neutral. The interviews showed that sensors should be small, not flashy and be compatible with everyday activities. Individuals without a current health risk or existing chronic disease showed lower preferences for using wearable sensors, particularly because they fear losing control over their own body. In contrast, individuals with increased risk or with an existing chronic disease were more likely to use wearable sensors as they can increase the personal safety and provide real-time health information to physicians. Therefore, an important deciding factor for and against the use of wearable sensors seems to be the perceived personal susceptibility for potential health problems.
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Affiliation(s)
- Corinne Matti
- Department Health Sciences and Medicine, University Lucerne, Lucerne, 6002, Switzerland.
- Institute of Social and Preventive Medicine, University Bern, Mittelstrasse 43, Bern, 3012, Switzerland.
| | - Stefan Essig
- Department Health Sciences and Medicine, University Lucerne, Lucerne, 6002, Switzerland
- Interface Politikstudien Forschung Beratung AG, Seidenhofstrasse 12, Lucerne, 6003, Switzerland
| | - Zora Föhn
- Department Health Sciences and Medicine, University Lucerne, Lucerne, 6002, Switzerland
- Interface Politikstudien Forschung Beratung AG, Seidenhofstrasse 12, Lucerne, 6003, Switzerland
| | - Andreas Balthasar
- Department Health Sciences and Medicine, University Lucerne, Lucerne, 6002, Switzerland
- Interface Politikstudien Forschung Beratung AG, Seidenhofstrasse 12, Lucerne, 6003, Switzerland
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5
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Zhao Q, Liu F, Song Y, Fan X, Wang Y, Yao Y, Mao Q, Zhao Z. Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model. Bioengineering (Basel) 2023; 10:1024. [PMID: 37760126 PMCID: PMC10525435 DOI: 10.3390/bioengineering10091024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.
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Affiliation(s)
- Qi Zhao
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (Q.Z.); (Y.W.)
| | - Fang Liu
- School of Information Technology, Dalian Maritime University, Dalian 116026, China; (F.L.); (Y.S.)
| | - Yide Song
- School of Information Technology, Dalian Maritime University, Dalian 116026, China; (F.L.); (Y.S.)
| | - Xiaoya Fan
- School of Software, Key Laboratory for Ubiquitous Network and Service Software, Dalian University of Technology, Dalian 116024, China;
| | - Yu Wang
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (Q.Z.); (Y.W.)
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA;
| | - Qian Mao
- School of Light Industry, Liaoning University, Shenyang 110136, China
| | - Zheng Zhao
- School of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
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6
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Oliver E, Yue R, Dutta A. A Secure Vitals Monitoring Point-of-Care Device. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083555 DOI: 10.1109/embc40787.2023.10340768] [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
Point-of-care (POC) devices continuously monitor vital signs and provide health suggestions to users. However, the devices are not affordable to everyone due to their cost. Here, we design a POC device that can continuously estimate vital signs using fewer sensors and lower costs. We do so by measuring photoplethysmogram signals and temperature and then estimating the heart rate, blood oxygen saturation, respiration rate, and blood pressure. For keeping the vital data secure, an auto-encoder and a convolutional neural network were also used for encryption and abnormality detection, respectively. Tests on the hardware showed the design accurately obtained users' vitals. The proposed design is expected to be generalized to obtain other vitals and fabricated at a low cost, making it affordable to all people.
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7
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Ejaz A, Jabeen I, Khan ZU, Alomainy A, Aljaloud K, Alqahtani AH, Hussain N, Hussain R, Amin Y. A High Performance All-Textile Wearable Antenna for Wristband Application. MICROMACHINES 2023; 14:1169. [PMID: 37374754 DOI: 10.3390/mi14061169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023]
Abstract
A compact, conformal, all-textile wearable antenna is proposed in this paper for the 2.45 GHz ISM (Industrial, Scientific and Medical) band. The integrated design consists of a monopole radiator backed by a 2 × 1 Electromagnetic Band Gap (EBG) array, resulting in a small form factor suitable for wristband applications. An EBG unit cell is optimized to work in the desired operating band, the results of which are further explored to achieve bandwidth maximization via floating EBG ground. A monopole radiator is made to work in association with the EBG layer to produce the resonance in the ISM band with plausible radiation characteristics. The fabricated design is tested for free space performance analysis and subjected to human body loading. The proposed antenna design achieves bandwidth of 2.39 GHz to 2.54 GHz with a compact footprint of 35.4 × 82.4 mm2. The experimental investigations reveal that the reported design adequately retains its performance while operating in close proximity to human beings. The presented Specific Absorption Rate (SAR) analysis reveals 0.297 W/kg calculated at 0.5 W input power, which certifies that the proposed antenna is safe for use in wearable devices.
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Affiliation(s)
- Asma Ejaz
- ACTSENA Research Group, Department of Telecommunication Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Iqra Jabeen
- ACTSENA Research Group, Department of Telecommunication Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Zia Ullah Khan
- Antenna and Electromagnetics Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London SE1 9DE, UK
| | - Akram Alomainy
- Antenna and Electromagnetics Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London SE1 9DE, UK
| | - Khaled Aljaloud
- College of Engineering, Muzahimiyah Branch, King Saud University, Riyadh 11451, Saudi Arabia
| | - Ali H Alqahtani
- College of Engineering, Muzahimiyah Branch, King Saud University, Riyadh 11451, Saudi Arabia
| | - Niamat Hussain
- Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | | | - Yasar Amin
- ACTSENA Research Group, Department of Telecommunication Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
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8
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Jegan R, Nimi WS. On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review. ZEITSCHRIFT FUR GESUNDHEITSWISSENSCHAFTEN = JOURNAL OF PUBLIC HEALTH 2023:1-16. [PMID: 37361281 PMCID: PMC10068243 DOI: 10.1007/s10389-023-01893-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/14/2023] [Indexed: 04/05/2023]
Abstract
Aim Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design considerations for wearable devices for early detection of health conditions. Subject and methods This article provides designers with information to identify and develop smart wearable devices based on the data extracted from a literature survey on previously published research articles in the field of wearable devices for monitoring vital parameters. Results The key information available in this article indicates that quality signal acquisition, processing and longtime monitoring of vital parameters requires smart wearable devices. The development of smart wearable devices with the listed design criteria supports the developer to design a low power wearable device for continuous monitoring of patient health conditions. Conclusion The wide range of information gathered from the review indicates that there is a huge demand for smart wearable devices for monitoring health conditions at home. It further supports tracking heath status in the long term via monitoring the vital parameters with the support of wireless communication principles.
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Affiliation(s)
- R. Jegan
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - W. S. Nimi
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
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9
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Liang X, Liu Y, Liu P, Yang J, Liu J, Yang Y, Wang B, Hu J, Zhang L, Yang G, Lu S, Liang G, Lan X, Zhang J, Gao L, Tang J. Large-area flexible colloidal-quantum-dot infrared photodiodes for photoplethysmogram signal measurements. Sci Bull (Beijing) 2023; 68:698-705. [PMID: 36931915 DOI: 10.1016/j.scib.2023.03.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/07/2023] [Accepted: 02/27/2023] [Indexed: 03/17/2023]
Abstract
Epitaxially grown photodiodes are the foundation of infrared photodetection technology; however, their rigid structure and limited area scaling limit their use in advanced applications. Colloidal-quantum-dot (CQD) infrared photodiodes have increased active areas through solution processing, and are thus potential candidates for large-area flexible photodetection, but these large-area photodiodes have disadvantages such as large dark current density, poor homogeneity, and poor stability. Therefore, this study established a fabrication strategy for large-area flexible CQD photodiodes that involves introducing polyimide to CQD ink to improve CQD passivation, monodisperse ink persistence, and film morphology. The resulting CQD photodiodes exhibited reduced dark current density and improved homogeneity and work stability. Furthermore, the as-prepared photodiodes exhibited a detectivity (D*) of greater than 1013 Jones, which was higher than other reported CQD photodetectors. The CQD photodiodes developed in this study can be used for wearable photoplethysmogram (PPG) signal measurement under ambient light at reduced cost and power consumption..
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Affiliation(s)
- Xinyi Liang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yuxuan Liu
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Peilin Liu
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Junrui Yang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jing Liu
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yang Yang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Bo Wang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jun Hu
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Linxiang Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Gaoyuan Yang
- Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices, Hubei University of Arts and Science, Xiangyang 441053, China
| | - Shuaicheng Lu
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China; Optics Valley Laboratory, Wuhan 430074, China; Wenzhou Advanced Manufacturing Technology Research Institute of Huazhong University of Science and Technology, Wenzhou 325006, China
| | - Guijie Liang
- Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices, Hubei University of Arts and Science, Xiangyang 441053, China
| | - Xinzheng Lan
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China; Optics Valley Laboratory, Wuhan 430074, China
| | - Jianbing Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China; Wenzhou Advanced Manufacturing Technology Research Institute of Huazhong University of Science and Technology, Wenzhou 325006, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518000, China.
| | - Liang Gao
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China; Optics Valley Laboratory, Wuhan 430074, China; Wenzhou Advanced Manufacturing Technology Research Institute of Huazhong University of Science and Technology, Wenzhou 325006, China.
| | - Jiang Tang
- Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China; Optics Valley Laboratory, Wuhan 430074, China
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10
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Gao F, Liu C, Zhang L, Liu T, Wang Z, Song Z, Cai H, Fang Z, Chen J, Wang J, Han M, Wang J, Lin K, Wang R, Li M, Mei Q, Ma X, Liang S, Gou G, Xue N. Wearable and flexible electrochemical sensors for sweat analysis: a review. MICROSYSTEMS & NANOENGINEERING 2023; 9:1. [PMID: 36597511 PMCID: PMC9805458 DOI: 10.1038/s41378-022-00443-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/26/2022] [Accepted: 08/10/2022] [Indexed: 06/10/2023]
Abstract
Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.
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Affiliation(s)
- Fupeng Gao
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Chunxiu Liu
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Lichao Zhang
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Tiezhu Liu
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Zheng Wang
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Zixuan Song
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Haoyuan Cai
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Zhen Fang
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Jiamin Chen
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Junbo Wang
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Mengdi Han
- Department of Biomedical Engineering, College of Future Technology, Peking University, 100871 Beijing, China
| | - Jun Wang
- Beijing Shuimujiheng Biotechnology Company, 101102 Beijing, China
| | - Kai Lin
- PLA Air Force Characteristic Medical Center, 100142 Beijing, China
| | - Ruoyong Wang
- PLA Air Force Characteristic Medical Center, 100142 Beijing, China
| | - Mingxiao Li
- Institute of Microelectronics of the Chinese Academy of Sciences, 100029 Beijing, China
| | - Qian Mei
- CAS Key Laboratory of Biomedical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences (CAS), 215163 Suzhou, China
| | - Xibo Ma
- CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Shuli Liang
- Functional Neurosurgery Department, Beijing Children’s Hospital, Capital Medical University, 100045 Beijing, China
| | - Guangyang Gou
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
| | - Ning Xue
- School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100190 Beijing, China
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100190 Beijing, China
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11
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Hu CL, Lin ZY, Hu SY, Cheng IC, Huang CH, Li YH, Li CJ, Lin CW. Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes. SENSORS (BASEL, SWITZERLAND) 2022; 22:9575. [PMID: 36559943 PMCID: PMC9782024 DOI: 10.3390/s22249575] [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: 11/16/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach.
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Affiliation(s)
- Chang-Lin Hu
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
| | - Zong-Yan Lin
- Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
| | - Shu-Yun Hu
- College of Law, National University of Kaohsiung, Kaohsiung 811, Taiwan
| | - I-Cheng Cheng
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan
| | - Chih-Hsien Huang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Yu-Hao Li
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Chien-Ju Li
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
| | - Chii-Wann Lin
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
- Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan
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12
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Salem M, Elkaseer A, El-Maddah IAM, Youssef KY, Scholz SG, Mohamed HK. Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176625. [PMID: 36081081 PMCID: PMC9460364 DOI: 10.3390/s22176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 05/06/2023]
Abstract
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.
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Affiliation(s)
- Mahmoud Salem
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: ; Tel.: +49-0-721-608-25632
| | - Ahmed Elkaseer
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Faculty of Engineering, Port Said University, Port Said 42526, Egypt
| | | | - Khaled Y. Youssef
- Faculty of Navigation Science and Space Technology, Beni-Suef University, Beni-Suef 2731070, Egypt
| | - Steffen G. Scholz
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- College of Engineering, Swansea University, Swansea SA2 8PP, UK
| | - Hoda K. Mohamed
- Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt
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13
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Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis. MICROMACHINES 2022; 13:mi13081225. [PMID: 36014147 PMCID: PMC9415520 DOI: 10.3390/mi13081225] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022]
Abstract
In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.
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14
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Verhaalen MA, Berry DT, Shea AR, McCallum KE, Dexheimer CA, Slinde CH, Rolli AC, Javan-Khoshkholgh A. A Wearable Wideband Analog Bio-Impedance Analyzer for Real-Time Monitoring of Human Physiology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:918-921. [PMID: 36086460 DOI: 10.1109/embc48229.2022.9871827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous monitoring of electrophysiological activities of the human body is a significant step toward the effective prognosis, diagnosis, and management of functional disorders and cardiovascular diseases. This paper presents the development of a wireless system for the real-time acquisition of hemodynamics data and ambulatory monitoring of body composition based on electrical bio-impedance (Bio-Z) analysis. The developed system is composed of a low-power wearable unit and a stationary unit connected to a computer. The system conducts the non-radiative non-invasive Bio-Z analysis over a wide bandwidth of 1 MHz through four independent channels. The proposed analog approach detects the physiological activity by extracting the magnitude of the mixed Bio-Z signal, in real-time. A graphical user interface was designed for monitoring, analysis, and storage of the processed data. Moreover, the amplitude and frequency of the electrical excitation signals can be instructed through the user interface, wirelessly. Bench-top validation of the system demonstrated the delivery of current signals over a wide frequency range of 1 kHz - 1 MHz and peak-to-peak amplitude of up to 20 mA. Besides, the system was able to detect the magnitude of the envelope of the mixed signal with amplitude modulation depths as low as 0.1 %. Clinical Relevance- The system provides the real-time monitoring of cardiac activity and blood pulsation in human arteries. In addition, due to the configurability of the frequency and amplitude of the current injection circuit, the system is an excellent candidate to be utilized for real-time medical imaging through electrical bio-impedance tomography as well as electrical bio-impedance spectroscopy.
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15
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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16
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Zhang ZQ, Zhang XL, Xu GS, Liu XJ, Guo Q, Feng Z, Jia JT, Ku PT. Fabrication of polydimethylsiloxane/graphene flexible strain sensors by using the scraping and coating method. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2022; 93:065001. [PMID: 35778021 DOI: 10.1063/5.0089849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
Production of flexible strain sensors is complex, time-consuming, and expensive. In this study, a novel fabrication method of polydimethylsiloxane/graphene nanocomposite conductive materials was proposed by using the scraping and coating method for manufacturing sandwich-shape flexible strain sensors. A ZQ-60B tensile testing machine was employed to test the mechanical properties of flexible sensors with 1%, 3%, and 5% graphene content. The results revealed that the stress and strain of the flexible strain sensor exhibited a linear relationship, and the linear correlation coefficients were 0.99706, 0.99819, and 0.99826, respectively. The concentration of graphene was 1%, 3%, and 5%, and the gauge factors (GFs) of the sensor were 24, 6, and 3, respectively. With the increase in the graphene content, the GF decreased gradually. This phenomenon could be attributed to tunneling, which increased the number of conductive pathways with an increase in the graphene content. Furthermore, the sensor exhibited excellent stability after 100 cycles of stretching/scaling. The finger joint bending test revealed that the flexible strain sensor is reproducible and exhibits excellent application prospects in monitoring human movement and health.
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Affiliation(s)
- Zhou Q Zhang
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Xue L Zhang
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Guang S Xu
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Xue J Liu
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Q Guo
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Z Feng
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Jiang T Jia
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
| | - Peng T Ku
- School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, No. 19, Jinhua south road, Xi'an, Shaanxi 710048, China
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17
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Cai E, Li D, Lin J, Li H. Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification. SENSORS 2022; 22:s22072794. [PMID: 35408408 PMCID: PMC9002565 DOI: 10.3390/s22072794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 02/01/2023]
Abstract
The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.
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Affiliation(s)
- Enjian Cai
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China;
| | - Dongsheng Li
- Department of Civil and Environmental Engineering, Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou 515063, China;
- Correspondence:
| | - Jianyuan Lin
- Department of Civil and Environmental Engineering, Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou 515063, China;
| | - Hongnan Li
- State Key Laboratory of Coastal & Offshore Engineering, Dalian University of Technology, Dalian 116023, China;
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18
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Kano S, Jarulertwathana N, Mohd-Noor S, Hyun JK, Asahara R, Mekaru H. Respiratory Monitoring by Ultrafast Humidity Sensors with Nanomaterials: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1251. [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] [MESH Headings] [Grants] [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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Affiliation(s)
- Shinya Kano
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa 277-0882, Japan;
- Sensing System Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8563, Japan
| | - Nutpaphat Jarulertwathana
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul 03760, Korea; (N.J.); (S.M.-N.); (J.K.H.)
| | - Syazwani Mohd-Noor
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul 03760, Korea; (N.J.); (S.M.-N.); (J.K.H.)
| | - Jerome K. Hyun
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul 03760, Korea; (N.J.); (S.M.-N.); (J.K.H.)
| | - Ryota Asahara
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8566, Japan;
| | - Harutaka Mekaru
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa 277-0882, Japan;
- Sensing System Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8563, Japan
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19
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Nantume A, Kiwanuka N, Muyinda A, Cauvel T, Shah S. Accuracy and reliability of a wireless vital signs monitor for hospitalized patients in a low-resource setting. Digit Health 2022; 8:20552076221102262. [PMID: 35656284 PMCID: PMC9152187 DOI: 10.1177/20552076221102262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/04/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The purpose of this study was to evaluate the accuracy and reliability of neoGuard in comparison to a conventional bedside monitor on patients in a low-resource clinical setting. Design This was a single-arm methods comparison study involving the use of a wearable vital signs monitor (neoGuardTM) versus a conventional bedside monitor (Edan iM8). Setting The study was conducted at Jinja Regional Referral Hospital, a tertiary care hospital situated in Eastern Uganda. Participants Thirty patients (10 male, 20 female) were enrolled from the adult recovery ward at JRRH. Participants were eligible for the study if they were at least 18 years of age, had 2 sets of normal vital sign measurements obtained 1 h apart, and were able and willing to provide informed consent. Main Outcome and Measures The primary outcome measures were (i) bias (mean deviation) and (ii) limits of agreement [95% CI]. Bland-Altman plots were generated to illustrate the level of agreement between the neoGuardTM technology and the Edan iM8 monitor. Results Bland-Altman analysis was performed for 24 participants; datasets from six participants were excluded due to missing or invalid measurements. Findings showed a moderate level of agreement for measurement of SpO2, PR, and RR, with >80% of subject means falling within the predefined acceptability limits. However, there was also notable variation in accuracy between subjects, with large standard deviations observed for measurement of all four parameters. While the level of agreement for measurement of temperature was low, this is partly explained by limitations in the comparison method.
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Affiliation(s)
| | - Noah Kiwanuka
- Department of Biostatistics and Epidemiology, Makerere University School of Public Health (MUSPH), Kampala, Uganda
| | - Asad Muyinda
- Jinja Regional Referral Hospital (JRRH), Jinja, Uganda
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20
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Hu CL, Cheng IC, Huang CH, Liao YT, Lin WC, Tsai KJ, Chi CH, Chen CW, Wu CH, Lin IT, Li CJ, Lin CW. Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography. SENSORS 2021; 21:s21206789. [PMID: 34696002 PMCID: PMC8537054 DOI: 10.3390/s21206789] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 11/16/2022]
Abstract
Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.
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Affiliation(s)
- Chang-Lin Hu
- Industrial Technology Research Institute, Hsinchu 310, Taiwan; (I.-C.C.); (K.-J.T.); (C.-J.L.); (C.-W.L.)
- Correspondence:
| | - I-Cheng Cheng
- Industrial Technology Research Institute, Hsinchu 310, Taiwan; (I.-C.C.); (K.-J.T.); (C.-J.L.); (C.-W.L.)
| | - Chih-Hsien Huang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (C.-H.H.); (C.-H.W.)
| | - Yu-Te Liao
- Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-T.L.); (I.-T.L.)
| | - Wei-Chieh Lin
- Division of Critical Care Medicine, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan; (W.-C.L.); (C.-W.C.)
| | - Kun-Ju Tsai
- Industrial Technology Research Institute, Hsinchu 310, Taiwan; (I.-C.C.); (K.-J.T.); (C.-J.L.); (C.-W.L.)
| | - Chih-Hsien Chi
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan;
| | - Chang-Wen Chen
- Division of Critical Care Medicine, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan; (W.-C.L.); (C.-W.C.)
| | - Chia-Hsi Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (C.-H.H.); (C.-H.W.)
| | - I-Te Lin
- Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-T.L.); (I.-T.L.)
| | - Chien-Ju Li
- Industrial Technology Research Institute, Hsinchu 310, Taiwan; (I.-C.C.); (K.-J.T.); (C.-J.L.); (C.-W.L.)
| | - Chii-Wann Lin
- Industrial Technology Research Institute, Hsinchu 310, Taiwan; (I.-C.C.); (K.-J.T.); (C.-J.L.); (C.-W.L.)
- Department of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan
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21
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Zhang J, Xu J, Lim J, Nolan JK, Lee H, Lee CH. Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management. Adv Healthc Mater 2021; 10:e2100194. [PMID: 33930258 DOI: 10.1002/adhm.202100194] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/12/2021] [Indexed: 12/11/2022]
Abstract
The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well-poised for the next generation of closed-loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on-tooth tattoos, skin-mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real-time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self-regulating closed-loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.
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Affiliation(s)
- Jinyuan Zhang
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
| | - Jian Xu
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
| | - Jongcheon Lim
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
| | - James K. Nolan
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
| | - Hyowon Lee
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
| | - Chi Hwan Lee
- Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA
- School of Mechanical Engineering School of Materials Engineering Purdue University West Lafayette IN 47907 USA
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22
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Cheng Y, Wang K, Xu H, Li T, Jin Q, Cui D. Recent developments in sensors for wearable device applications. Anal Bioanal Chem 2021; 413:6037-6057. [PMID: 34389877 DOI: 10.1007/s00216-021-03602-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/26/2021] [Accepted: 08/04/2021] [Indexed: 01/23/2023]
Abstract
Wearable devices are a new means of human-computer interaction with different functions, underlying principles, and forms. They have been widely used in the medical and health fields, in applications including physiological signal monitoring; sports; and environmental detection, while subtly affecting people's lives and work. Wearable sensors as functional components of wearable devices have become a research focus. In this review, we systematically summarize recent progress in the development of wearable sensors and related devices. Wearable sensors in medical health applications, according to the principle of measurement, are divided into physical and chemical quantity detection. These sensors can monitor and measure specific parameters, thereby enabling continuously improvements in the quality and feasibility of medical treatment. Through the detection of human movement, such as breathing, heartbeat, or bending, wearable sensors can evaluate body movement and monitor an individual's physical performance and health status. Wearable devices detecting aspects of the environment while maintaining high adaptability to the human body can be used to evaluate environmental quality and obtain more accurate environmental information. The ultimate goal of this review is to provide new insights and directions for the future development and broader application of wearable devices in various fields.Graphical abstract.
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Affiliation(s)
- Yuemeng Cheng
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kan Wang
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Hao Xu
- School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Tangan Li
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qinghui Jin
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.,Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Daxiang Cui
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
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Vila G, Godin C, Charbonnier S, Campagne A. Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications. SENSORS 2021; 21:s21165357. [PMID: 34450799 PMCID: PMC8400129 DOI: 10.3390/s21165357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/22/2021] [Accepted: 08/03/2021] [Indexed: 01/02/2023]
Abstract
Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.
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Affiliation(s)
- Gaël Vila
- Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France;
- Gipsa-Lab, Univ. Grenoble Alpes & CNRS, F-38402 Grenoble, France;
| | - Christelle Godin
- Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France;
- Correspondence: ; Tel.: +33-438-784-067
| | | | - Aurélie Campagne
- LPNC UMR 5105, Univ. Grenoble Alpes & CNRS, F-38040 Grenoble, France;
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Wang N, Daniels R, Connelly L, Sotzing M, Wu C, Gerhard R, Sotzing GA, Cao Y. All-Organic Flexible Ferroelectret Nanogenerator with Fabric-Based Electrodes for Self-Powered Body Area Networks. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103161. [PMID: 34270880 DOI: 10.1002/smll.202103161] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/17/2021] [Indexed: 06/13/2023]
Abstract
Due to their electrically polarized air-filled internal pores, optimized ferroelectrets exhibit a remarkable piezoelectric response, making them suitable for energy harvesting. Expanded polytetrafluoroethylene (ePTFE) ferroelectret films are laminated with two fluorinated-ethylene-propylene (FEP) copolymer films and internally polarized by corona discharge. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-coated spandex fabric is employed for the electrodes to assemble an all-organic ferroelectret nanogenerator (FENG). The outer electret-plus-electrode double layers form active device layers with deformable electric dipoles that strongly contribute to the overall piezoelectric response in the proposed concept of wearable nanogenerators. Thus, the FENG with spandex electrodes generates a short-circuit current which is twice as high as that with aluminum electrodes. The stacking sequence spandex/FEP/ePTFE/FEP/ePTFE/FEP/spandex with an average pore size of 3 µm in the ePTFE films yields the best overall performance, which is also demonstrated by the displacement-versus-electric-field loop results. The all-organic FENGs are stable up to 90 °C and still perform well 9 months after being polarized. An optimized FENG makes three light emitting diodes (LEDs) blink twice with the energy generated during a single footstep. The new all-organic FENG can thus continuously power wearable electronic devices and is easily integrated, for example, with clothing, other textiles, or shoe insoles.
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Affiliation(s)
- Ningzhen Wang
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
| | - Robert Daniels
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
| | - Liam Connelly
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Michael Sotzing
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Chao Wu
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
| | - Reimund Gerhard
- Institute of Physics and Astronomy, Faculty of Science, University of Potsdam, 14476, Potsdam-Golm, Germany
| | - Gregory A Sotzing
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
- Polymer Program, University of Connecticut, Storrs, CT, 06269, USA
| | - Yang Cao
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, 06269, USA
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25
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Khan AN, Ihalage AA, Ma Y, Liu B, Liu Y, Hao Y. Deep learning framework for subject-independent emotion detection using wireless signals. PLoS One 2021; 16:e0242946. [PMID: 33534826 PMCID: PMC7857608 DOI: 10.1371/journal.pone.0242946] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/12/2020] [Indexed: 11/18/2022] Open
Abstract
Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67% for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.
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Affiliation(s)
- Ahsan Noor Khan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Achintha Avin Ihalage
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Yihan Ma
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Baiyang Liu
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Yujie Liu
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Yang Hao
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
- * E-mail:
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Basjaruddin NC, Syahbarudin F, Sutjiredjeki E. Measurement Device for Stress Level and Vital Sign Based on Sensor Fusion. Healthc Inform Res 2021; 27:11-18. [PMID: 33611872 PMCID: PMC7921569 DOI: 10.4258/hir.2021.27.1.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 12/22/2020] [Indexed: 11/23/2022] Open
Abstract
Objectives Medical health monitoring generally refers to two important aspects of health, namely, physical and mental health. Physical health can be measured through the basic parameters of normal values of vital signs, while mental health can be known from the prevalence of mental and emotional disorders, such as stress. Currently, the medical devices that are generally used to measure these two aspects of health are still separate, so they are less effective than they might be otherwise. To overcome this problem, we designed and realized a device that can measure stress levels through vital signs of the body, namely, heart rate, oxygen saturation, body temperature, and galvanic skin response (GSR). Methods The sensor fusion method is used to process data from multiple sensors, so the output that shows the stress level and health status of vital signs can be more accurate and precise. Results Based on the results of testing, this device is able to show the health status of vital signs and stress levels within ±20 seconds, with the accuracies of body temperature measurements, oxygen saturation, and GSR of 97.227%, 99.4%, and 98.6%, respectively. Conclusions A device for the measurement of stress levels and vital signs based on sensor fusion has been successfully designed and realized in accordance with the expected functions and specifications.
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Affiliation(s)
| | - Febian Syahbarudin
- Department of Electrical Engineering, Politeknik Negeri Bandung, Bandung, Indonesia
| | - Ediana Sutjiredjeki
- Department of Electrical Engineering, Politeknik Negeri Bandung, Bandung, Indonesia
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27
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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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Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique. ELECTRONICS 2020. [DOI: 10.3390/electronics9111850] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.
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29
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Arquilla K, Webb AK, Anderson AP. Woven electrocardiogram (ECG) electrodes for health monitoring in operational environments. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4498-4501. [PMID: 33018993 DOI: 10.1109/embc44109.2020.9176478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Electrical signals produced within the human body can reveal information about a wide variety of physiological processes including physical activity, cardiac health, and psychological state. The industry standard for physiological signal detection is the use of adhesive electrodes that stick onto the skin. These electrodes can irritate the skin over long periods of time and are not reusable, making them a challenge for use in operational environments. Further, these electrodes often require gel to improve signal transduction, leading to changes in signal quality as these gels dry over time. Wearable sensors for operational environments should be comfortable, unobtrusive, and non-stigmatizing while maintaining signal quality high enough to allow the detection of health states. Here, we present the development and test of a set of woven textile electrodes of 8 different sizes for chest-mounted, 3-lead electrocardiogram (ECG) monitoring. Ten male subjects were tested with each of the woven electrode sizes and with one set of adhesive electrodes. A derived performance metric and signal-to-noise ratio were calculated for each set of electrodes for comparison between them. The smallest sized electrodes were found to be least effective, while the 6th of the 8 sizes were found to be most effective.
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30
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Pais B, Buluschek P, DuPasquier G, Nef T, Schütz N, Saner H, Gatica-Perez D, Santschi V. Evaluation of 1-Year in-Home Monitoring Technology by Home-Dwelling Older Adults, Family Caregivers, and Nurses. Front Public Health 2020; 8:518957. [PMID: 33134236 PMCID: PMC7562920 DOI: 10.3389/fpubh.2020.518957] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 08/14/2020] [Indexed: 11/13/2022] Open
Abstract
Introduction: Population aging is increasing the needs and costs of healthcare. Both frailty and the chronic diseases affecting older people reduce their ability to live independently. However, most older people prefer to age in their own homes. New development of in-home monitoring can play a role in staying independent, active, and healthy for older people. This 12-month observational study aimed to evaluate a new in-home monitoring system among home-dwelling older adults (OA), their family caregivers (FC), and nurses for the support of home care. Methods: The in-home monitoring system evaluated in this study continuously monitored OA's daily activities (e.g., mobility, sleep habits, fridge visits, door events) by ambient sensor system (DomoCare®) and health-related events by wearable sensors (Activity tracker, ECG). In the case of deviations in daily activities, alerts were transmitted to nurses via email. Using specific questionnaires, the opinions of 13 OA, 13 FC, and 20 nurses were collected at the end of 12-months follow-up focusing on user experience and the impact of in-home monitoring on home care services. Results: The majority of OA, FC, and nurses considered that in-home sensors can help with staying at home, improving home care and quality of life, preventing domestic accidents, and reducing family stress. The opinion tended to be more frequently favorable toward ambient sensors (76%; 95% CI: 61-87%) than toward wearable sensors (Activity tracker: 65%; 95% CI: 50-79%); ECG: 60%; 95% CI: 45-75%). On average, OA (74%; 95% CI: 46-95%) and FC (70%; 95% CI: 39-91%) tended to be more enthusiastic than nurses (60%; 95% CI: 36-81%). Some barriers reported by nurses were a fear of weakening of the relationship with OA and lack of time. Discussion/Conclusion: Overall, the opinions of OA, FC, and nurses were positively related to in-home sensors, with nurses being less enthusiastic about their use in clinical practice.
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Affiliation(s)
- Bruno Pais
- La Source, School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | | | | | - Tobias Nef
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Narayan Schütz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Hugo Saner
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Cardiology, University Hospital Bern, Bern, Switzerland
| | - Daniel Gatica-Perez
- Idiap Research Institute, Martigny, Switzerland.,School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Valérie Santschi
- La Source, School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
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Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165470] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.
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32
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Kedambaimoole V, Kumar N, Shirhatti V, Nuthalapati S, Sen P, Nayak MM, Rajanna K, Kumar S. Laser-Induced Direct Patterning of Free-standing Ti 3C 2-MXene Films for Skin Conformal Tattoo Sensors. ACS Sens 2020; 5:2086-2095. [PMID: 32551595 DOI: 10.1021/acssensors.0c00647] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The discovery of stable two-dimensional (2D) materials has effectuated a rapid evolution of skin conformal sensors for health monitoring via epidermal electronics. Among the newly discovered 2D materials, MXene stands out as a solution-processable 2D material allowing easy fabrication of highly conductive thin films with the potential to realize flexible skin conformal sensors. Here, we present a successful demonstration of a Ti3C2-MXene resistor as an extremely sensitive strain sensor in the form an ultrathin skin mountable temporary tattoo. The skin conformability and form factor afforded by the sensor promises inconspicuous and continuous monitoring of vital health parameters of an individual, like the pulse rate, respiration rate, and surface electromyography. The sensor serves as a single conduit for sensing the respiration rate and pulse, dispensing with the need of mounting multiple sensors. Its remarkably high sensitivity with a gauge factor of ∼7400 has been ascribed to development of nanocracks and their propagation through the film upon application of strain. The fast response and highly repeatable sensor follows easy fabrication steps and can be patterned into any shape and size using a laser.
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Affiliation(s)
- Vaishakh Kedambaimoole
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Neelotpala Kumar
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Vijay Shirhatti
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Suresh Nuthalapati
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Prosenjit Sen
- Centre for Nano Science and Engineering (CeNSE), Indian Institute of Science, Bengaluru 560012, India
| | | | - Konandur Rajanna
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Saurabh Kumar
- Centre for Nano Science and Engineering (CeNSE), Indian Institute of Science, Bengaluru 560012, India
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Tscholl DW, Rössler J, Said S, Kaserer A, Spahn DR, Nöthiger CB. Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology: A Qualitative Review of the Primary Research. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2112. [PMID: 32283625 PMCID: PMC7180744 DOI: 10.3390/s20072112] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/01/2020] [Accepted: 04/04/2020] [Indexed: 12/15/2022]
Abstract
Visual Patient technology is a situation awareness-oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements-a language humans can easily perceive and interpret-on a patient avatar model in real time. In this review, we summarize the current state of the research on the Visual Patient, including the technology, its history, and its scientific context. We also provide a summary of our primary research and a brief overview of research work on similar user-centered visualizations in medicine. In several computer-based studies under various experimental conditions, Visual Patient transferred more information per unit time, increased perceived diagnostic certainty, and lowered perceived workload. Eye tracking showed the technology worked because of the way it synthesizes and transforms vital sign information into new and logical forms corresponding to the real phenomena. The technology could be particularly useful for improving situation awareness in settings with high cognitive demand or when users must make quick decisions. This comprehensive review of Visual Patient research is the foundation for an evaluation of the technology in clinical applications, starting with a high-fidelity simulation study in early 2020.
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Affiliation(s)
- David Werner Tscholl
- Institute of Anesthesiology, University and University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland; (J.R.); (S.S.); (A.K.); (D.R.S.); (C.B.N.)
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34
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Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04862-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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35
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Arquilla K, Webb AK, Anderson AP. Textile Electrocardiogram (ECG) Electrodes for Wearable Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1013. [PMID: 32069937 PMCID: PMC7070603 DOI: 10.3390/s20041013] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/06/2020] [Accepted: 02/11/2020] [Indexed: 11/29/2022]
Abstract
Wearable health-monitoring systems should be comfortable, non-stigmatizing, and able to achieve high data quality. Smart textiles with electronic elements integrated directly into fabrics offer a way to embed sensors into clothing seamlessly to serve these purposes. In this work, we demonstrate the feasibility of electrocardiogram (ECG) monitoring with sewn textile electrodes instead of traditional gel electrodes in a 3-lead, chest-mounted configuration. The textile electrodes are sewn with silver-coated thread in an overlapping zig zag pattern into an inextensible fabric. Sensor validation included ECG monitoring and comfort surveys with human subjects, stretch testing, and wash cycling. The electrodes were tested with the BIOPAC MP160 ECG data acquisition module. Sensors were placed on 8 subjects (5 males and 3 females) with double-sided tape. To detect differences in R peak detectability between traditional and sewn sensors, effect size was set at 10% of a sample mean for heart rate (HR) and R-R interval. Paired student's t-tests were run between adhesive and sewn electrode data for R-R interval and average HR, and a Wilcoxon signed-rank test was run for comfort. No statistically significant difference was found between the traditional and textile electrodes (R-R interval: t = 1.43, p > 0.1; HR: t = - 0.70, p > 0.5; comfort: V = 15,p > 0.5).
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Affiliation(s)
- Katya Arquilla
- Ann and H. J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80303, USA;
- The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139, USA;
| | - Andrea K. Webb
- The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139, USA;
| | - Allison P. Anderson
- Ann and H. J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80303, USA;
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36
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Simultaneous piezoelectric noninvasive detection of multiple vital signs. Sci Rep 2020; 10:416. [PMID: 31942021 PMCID: PMC6962459 DOI: 10.1038/s41598-019-57326-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 12/26/2019] [Indexed: 11/09/2022] Open
Abstract
The monitoring of vital signs plays a key role in the diagnosis of several diseases. Piezoelectric sensors have been utilized to collect a corresponding representative signal from the chest surface. The subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. The induced vibration is then captured by the piezoelectric sensor placed at the chest surface, which produces an electrical output voltage signal conformally mapped with the respiration-cardiac activities. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. A representative model that incorporates the cardiac and respiratory activities is developed and adopted. The piezoelectric and the convolution theories along with Fourier transformation are applied to extract the corresponding cardiac activity signal from the respiration signal. All the results were validated step by step by a conventional apparatus, with good agreement observed.
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Haseda Y, Bonefacino J, Tam HY, Chino S, Koyama S, Ishizawa H. Measurement of Pulse Wave Signals and Blood Pressure by a Plastic Optical Fiber FBG Sensor. SENSORS 2019; 19:s19235088. [PMID: 31766391 PMCID: PMC6928766 DOI: 10.3390/s19235088] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 11/10/2019] [Accepted: 11/18/2019] [Indexed: 11/16/2022]
Abstract
Fiber Bragg grating (FBG) sensors fabricated in silica optical fiber (Silica-FBG) have been used to measure the strain of human arteries as pulse wave signals. A variety of vital signs including blood pressure can be derived from these signals. However, silica optical fiber presents a safety risk because it is easily fractured. In this research, an FBG sensor fabricated in plastic optical fiber (POF-FBG) was employed to resolve this problem. Pulse wave signals were measured by POF-FBG and silica-FBG sensors for four subjects. After signal processing, a calibration curve was constructed by partial least squares regression, then blood pressure was calculated from the calibration curve. As a result, the POF-FBG sensor could measure the pulse wave signals with an signal to noise (SN) ratio at least eight times higher than the silica-FBG sensor. Further, the measured signals were substantially similar to those of an acceleration plethysmograph (APG). Blood pressure is measured with low error, but the POF-FBG APG correlation is distributed from 0.54 to 0.72, which is not as high as desired. Based on these results, pulse wave signals should be measured under a wide range of reference blood pressures to confirm the reliability of blood pressure measurement uses POF-FBG sensors.
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Affiliation(s)
- Yuki Haseda
- Gratuate School of Medicine, Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan;
| | - Julien Bonefacino
- Department of Electrical Engineering, Photonics Research Centre, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China; (J.B.); (H.-Y.T.)
| | - Hwa-Yaw Tam
- Department of Electrical Engineering, Photonics Research Centre, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China; (J.B.); (H.-Y.T.)
| | - Shun Chino
- Interdisciplinary Graduate School of Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan;
| | - Shouhei Koyama
- Faculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan
- Correspondence: ; Tel.: +81-268-21-5603
| | - Hiroaki Ishizawa
- Institute for Fiber Engineering, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan;
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McLamore ES, Palit Austin Datta S, Morgan V, Cavallaro N, Kiker G, Jenkins DM, Rong Y, Gomes C, Claussen J, Vanegas D, Alocilja EC. SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems. SENSORS 2019; 19:s19224935. [PMID: 31766116 PMCID: PMC6891700 DOI: 10.3390/s19224935] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 12/16/2022]
Abstract
In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.
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Affiliation(s)
- Eric S. McLamore
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
- Correspondence: ; Tel.: +1-(352)294-6703
| | - Shoumen Palit Austin Datta
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
- MIT Auto-ID Labs, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- MDPnP Labs, Biomedical Engineering Program, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139, USA
| | - Victoria Morgan
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
| | - Nicholas Cavallaro
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
| | - Greg Kiker
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
| | - Daniel M. Jenkins
- Molecular Biosciences and Bioengineering, University of Hawaii Manoa, Honolulu, HI 96822, USA;
| | - Yue Rong
- Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA or (V.M.); (N.C.); (G.K.); (Y.R.)
| | - Carmen Gomes
- Mechanical Engineering, Iowa State University, Ames, IA 50011, USA;
| | - Jonathan Claussen
- Mechanical Engineering Department, Iowa State University, Ames, IA 50011, USA;
- Ames Laboratory, Ames, IA 50011, USA
| | - Diana Vanegas
- Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29634, USA;
| | - Evangelyn C. Alocilja
- Global Alliance for Rapid Diagnostics, Michigan State University, East Lansing, MI 48824, USA;
- Nano-Biosensors Lab, Michigan State University, East Lansing, MI 48824, USA
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Suntornsuk W, Suntornsuk L. Recent applications of paper‐based point‐of‐care devices for biomarker detection. Electrophoresis 2019; 41:287-305. [DOI: 10.1002/elps.201900258] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 09/30/2019] [Accepted: 10/05/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Worapot Suntornsuk
- Department of Microbiology, Faculty of ScienceKing Mongkut's University of Technology Thonburi Bangkok Thailand
| | - Leena Suntornsuk
- Department of Pharmaceutical ChemistryFaculty of PharmacyMahidol University Bangkok Thailand
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Chen J. Research on evaluating the design effect of clothing and accessories with 2-tuple linguistic information. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-179269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jia Chen
- School of Architecture and Design, Changchun Institute of Technology, Changchun, China
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Safavi KC, Driscoll W, Wiener-Kronish JP. Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time. Anesth Analg 2019; 129:726-734. [PMID: 31425213 PMCID: PMC6693927 DOI: 10.1213/ane.0000000000003948] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2018] [Indexed: 01/11/2023]
Abstract
The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.
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Affiliation(s)
- Kyan C. Safavi
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William Driscoll
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeanine P. Wiener-Kronish
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Chirra M, Marsili L, Wattley L, Sokol LL, Keeling E, Maule S, Sobrero G, Artusi CA, Romagnolo A, Zibetti M, Lopiano L, Espay AJ, Obeidat AZ, Merola A. Telemedicine in Neurological Disorders: Opportunities and Challenges. Telemed J E Health 2019; 25:541-550. [PMID: 30136898 PMCID: PMC6664824 DOI: 10.1089/tmj.2018.0101] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/28/2018] [Accepted: 06/29/2018] [Indexed: 12/23/2022] Open
Abstract
Introduction: Telemedicine represents an emerging model for the assessment and management of various neurological disorders. Methods: We sought to discuss opportunities and challenges for the integration of telemedicine in the management of common and uncommon neurological disorders by reviewing and appraising studies that evaluate telemedicine as a means to facilitate the access to care, deliver highly specialized visits, diagnostic consultations, rehabilitation, and remote monitoring of neurological disorders. Results: Opportunities for telemedicine in neurological disorders include the replacement of or complement to in-office evaluations, decreased time between follow-up visits, reduction in disparities in access to healthcare, and promotion of education and training through interactions between primary care physicians and tertiary referral centers. Critical challenges include the integration of the systems for data monitoring with an easy-to-use, secure, and cost-effective platform that is both widely adopted by patients and healthcare systems and embraced by international scientific societies. Conclusions: Multiple applications may spawn from a model based on digitalized healthcare services. Integrated efforts from multiple stakeholders will be required to develop an interoperable software platform capable of providing not only a holistic approach to care but also one that reduces disparities in the access to care.
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Affiliation(s)
- Martina Chirra
- Division of Hematology-Oncology, Department of Internal Medicine, University of Cincinnati, Cincinnati, Ohio
- Department of Oncology, Medical Oncology Unit, University of Siena, Siena, Italy
| | - Luca Marsili
- Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio
| | - Linsdey Wattley
- College of Medicine, University of Cincinnati, Cincinnati, Ohio
| | - Leonard L. Sokol
- Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio
- College of Medicine, University of Cincinnati, Cincinnati, Ohio
| | - Elizabeth Keeling
- Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio
| | - Simona Maule
- Autonomic Unit, Department of Medical Sciences, Città della Salute e della Scienza Hospital, Torino, Italy
| | - Gabriele Sobrero
- Autonomic Unit, Department of Medical Sciences, Città della Salute e della Scienza Hospital, Torino, Italy
| | - Carlo Alberto Artusi
- Department of Neuroscience Rita Levi Montalcini, University of Turin, Torin, Italy
| | - Alberto Romagnolo
- Department of Neuroscience Rita Levi Montalcini, University of Turin, Torin, Italy
| | - Maurizio Zibetti
- Department of Neuroscience Rita Levi Montalcini, University of Turin, Torin, Italy
| | - Leonardo Lopiano
- Department of Neuroscience Rita Levi Montalcini, University of Turin, Torin, Italy
| | - Alberto J. Espay
- Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio
| | - Ahmed Z. Obeidat
- Department of Neurology and Rehabilitation Medicine, The Waddell Center for Multiple Sclerosis, University of Cincinnati, Cincinnati, Ohio
| | - Aristide Merola
- Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio
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Chern CC, Chen YJ, Hsiao B. Decision tree-based classifier in providing telehealth service. BMC Med Inform Decis Mak 2019; 19:104. [PMID: 31146749 PMCID: PMC6543775 DOI: 10.1186/s12911-019-0825-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 05/21/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Although previous research showed that telehealth services can reduce the misuse of resources and urban-rural disparities, most healthcare insurers do not include telehealth services in their health insurance schemes. Therefore, no target variable exists for the classification approaches to learn from or train with. The problem of identifying the potential recipients of telehealth services when introducing telehealth services into health welfare or health insurance schemes becomes an unsupervised classification problem without a target variable. METHODS We propose a HDTTCA approach, which is a systematic approach (the main process of HDTTCA involves (1) data set preprocessing, (2) decision tree model building, and (3) predicting and explaining of the most important attributes in the data set for patients who qualify for telehealth service) to identify those who are eligible for telehealth services. RESULTS This work uses data from the NHIRD provided by the NHIA in Taiwan in 2012 as our research scope, which consist of 55,389 distinct hospitals and 653,209 distinct patients with 15,882,153 outpatient and 135,775 inpatient records. After HDTTCA produces the final version of the decision tree, the rules can be used to assign the values of the target variables in the entire NHIRD. Our data indicate that 3.56% (23,262 out of 653,209) of the patients are eligible for telehealth services in 2012. This study verifies the efficiency and validity of HDTTCA by using a large data set from the NHI of Taiwan. CONCLUSION This study conducts a series of experiments 30 times to compare the HDTTCA results with the logistic regression findings by measuring their average performance and determining which model addresses the telehealth patient classification problem better. Four important metrics are used to compare the results. In terms of sensitivity, the decision trees generated by HDTTCA and the logistic regression model are on equal grounds. In terms of accuracy, specificity, and precision, the decision tree generated by HDTTCA provides a better performance than that of the logistic regression model. When HDTTCA is applied, the decision tree model generates a competitive performance and provides clear, easily understandable rules. Therefore, HDTTCA is a suitable choice in solving telehealth service classification problems.
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Affiliation(s)
- Ching-Chin Chern
- Department of Information Management, National Taiwan University, 50, Lane 144, Sec. 4, Keelung Road, Taipei, 106 Taiwan
| | - Yu-Jen Chen
- Department of Information Management, National Taiwan University, 50, Lane 144, Sec. 4, Keelung Road, Taipei, 106 Taiwan
| | - Bo Hsiao
- Department of Information Management, Chang Jung Christian University, No.1,Changda Rd., Gueiren District, Tainan City, 71101 Taiwan
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Abstract
A miniaturized continuous-wave Doppler radar sensor operating at 915 MHz to remotely detect both respiration and heart rate (beats per minute) is presented. The proposed radar sensor comprises a front-end module including an implemented complementary metal-oxide semiconductor low-noise amplifier (LNA) and fractal-slot patch antennas, whose area was reduced by 15.2%. The two-stage inverter-based LNA was designed with an interstage capacitor and a feedback resistor to acquire ultrawide bandwidth. Two operating frequencies, 915 MHz and 2.45 GHz, were analyzed with regard to path loss for efficient operation because frequency affects detection sensitivity, reflected signal power from the human body, and measurement distance in a far-field condition. Path-loss calculation based on the simplified layer model indicates that the reflected power of the 915 MHz radar could be higher than that of the 2.45 GHz radar. The implemented radar front-end module excluding the LNA occupies 35 × 55 mm2. Vital signs were obtained via a fast Fourier transform and digital filtering using raw signals. In an experiment with six subjects, the respiration and heart rate obtained at 0.8 m using the proposed radar sensor exhibited mean accuracies of 99.4% and 97.6% with respect to commercialized reference sensors, respectively.
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45
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Salivary diagnostics on paper microfluidic devices and their use as wearable sensors for glucose monitoring. Anal Bioanal Chem 2019; 411:4919-4928. [DOI: 10.1007/s00216-019-01788-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 03/12/2019] [Accepted: 03/15/2019] [Indexed: 12/24/2022]
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Salinas Alvarez C, Sierra-Sosa D, Garcia-Zapirain B, Yoder-Himes D, Elmaghraby A. Detection of Volatile Compounds Emitted by Bacteria in Wounds Using Gas Sensors. SENSORS 2019; 19:s19071523. [PMID: 30925832 PMCID: PMC6480681 DOI: 10.3390/s19071523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/19/2019] [Accepted: 03/26/2019] [Indexed: 11/15/2022]
Abstract
In this paper we analyze an experiment for the use of low-cost gas sensors intended to detect bacteria in wounds using a non-intrusive technique. Seven different genera/species of microbes tend to be present in most wound infections. Detection of these bacteria usually requires sample and laboratory testing which is costly, inconvenient and time-consuming. The validation processes for these sensors with nineteen types of microbes (1 Candida, 2 Enterococcus, 6 Staphylococcus, 1 Aeromonas, 1 Micrococcus, 2 E. coli and 6 Pseudomonas) are presented here, in which four sensors were evaluated: TGS-826 used for ammonia and amines, MQ-3 used for alcohol detection, MQ-135 for CO2 and MQ-138 for acetone detection. Validation was undertaken by studying the behavior of the sensors at different distances and gas concentrations. Preliminary results with liquid cultures of 108 CFU/mL and solid cultures of 108 CFU/cm2 of the 6 Pseudomonas aeruginosa strains revealed that the four gas sensors showed a response at a height of 5 mm. The ammonia detection response of the TGS-826 to Pseudomonas showed the highest responses for the experimental samples over the background signals, with a difference between the values of up to 60 units in the solid samples and the most consistent and constant values. This could suggest that this sensor is a good detector of Pseudomonas aeruginosa, and the recording made of its values could be indicative of the detection of this species. All the species revealed similar CO2 emission and a high response rate with acetone for Micrococcus, Aeromonas and Staphylococcus.
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Affiliation(s)
| | - Daniel Sierra-Sosa
- Department of Computer Engineering and Computer Science (CECS), University of Louisville, Louisville, KY 40292, USA.
| | | | | | - Adel Elmaghraby
- Department of Computer Engineering and Computer Science (CECS), University of Louisville, Louisville, KY 40292, USA.
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Peng Z, Li C. Portable Microwave Radar Systems for Short-Range Localization and Life Tracking: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1136. [PMID: 30845720 PMCID: PMC6427700 DOI: 10.3390/s19051136] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/01/2019] [Accepted: 03/01/2019] [Indexed: 12/02/2022]
Abstract
Short-range localization and life tracking have been hot research topics in the fields of medical care, consumer electronics, driving assistance, and indoor robots/drones navigation. Among various sensors, microwave and mm-wave continuous-wave (CW) radar sensors are gaining more popularity in their intrinsic advantages such as simple architecture, easy system integration, high accuracy, relatively low cost, and penetration capability. This paper reviews the recent advances in CW radar systems for short-range localization and life tracking applications, including system improvement, signal processing, as well as the emerging applications integrated with machine learning.
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Affiliation(s)
| | - Changzhi Li
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
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Chu M, Nguyen T, Pandey V, Zhou Y, Pham HN, Bar-Yoseph R, Radom-Aizik S, Jain R, Cooper DM, Khine M. Respiration rate and volume measurements using wearable strain sensors. NPJ Digit Med 2019; 2:8. [PMID: 31304358 PMCID: PMC6550208 DOI: 10.1038/s41746-019-0083-3] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 01/15/2019] [Indexed: 12/31/2022] Open
Abstract
Current methods for continuous respiration monitoring such as respiratory inductive or optoelectronic plethysmography are limited to clinical or research settings; most wearable systems reported only measures respiration rate. Here we introduce a wearable sensor capable of simultaneously measuring both respiration rate and volume with high fidelity. Our disposable respiration sensor with a Band-Aid© like formfactor can measure both respiration rate and volume by simply measuring the local strain of the ribcage and abdomen during breathing. We demonstrate that both metrics are highly correlated to measurements from a medical grade continuous spirometer on participants at rest. Additionally, we also show that the system is capable of detecting respiration under various ambulatory conditions. Because these low-powered piezo-resistive sensors can be integrated with wireless Bluetooth units, they can be useful in monitoring patients with chronic respiratory diseases in everyday settings.
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Affiliation(s)
- Michael Chu
- Department of Biomedical Engineering, University of California, Irvine, CA USA
| | - Thao Nguyen
- Department of Chemical Engineering, University of California, Irvine, CA USA
| | - Vaibhav Pandey
- Bren School of Information and Computer Sciences, University of California, Irvine, CA USA
| | - Yongxiao Zhou
- Department of Biomedical Engineering, University of California, Irvine, CA USA
| | - Hoang N. Pham
- Pediatric Exercise and Genomics Research Center, School of Medicine, University of California, Irvine, CA USA
| | - Ronen Bar-Yoseph
- Pediatric Exercise and Genomics Research Center, School of Medicine, University of California, Irvine, CA USA
- Department of Pediatrics, Rambam Medical Center, Haifa, Israel
| | - Shlomit Radom-Aizik
- Pediatric Exercise and Genomics Research Center, School of Medicine, University of California, Irvine, CA USA
| | - Ramesh Jain
- Bren School of Information and Computer Sciences, University of California, Irvine, CA USA
| | - Dan M. Cooper
- Pediatric Exercise and Genomics Research Center, School of Medicine, University of California, Irvine, CA USA
| | - Michelle Khine
- Department of Biomedical Engineering, University of California, Irvine, CA USA
- Department of Chemical Engineering, University of California, Irvine, CA USA
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Li H, Zhang C, Feng X. Monte Carlo simulation of light scattering in tissue for the design of skin-like optical devices. BIOMEDICAL OPTICS EXPRESS 2019; 10:868-878. [PMID: 30800520 PMCID: PMC6377905 DOI: 10.1364/boe.10.000868] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/31/2018] [Accepted: 11/22/2018] [Indexed: 05/05/2023]
Abstract
Measurement techniques based on optics, with the characteristics of noninvasive or non-destructive detection and high accuracy, offer excellent properties for application in various scenarios. Skin-like optical devices capable of deforming with human skin play major roles in future biomedical applications such as clinical diagnostics or biological healthcare. Unlike traditional rigid devices, the skin-like optical device is conformal to the skin because of the flexibility and stretchability. However, the detected signals based on light intensity are very sensitive to the light path. As a result, the accuracy and efficiency of the skin-like device will be influenced owing to deformation. In this work, for optimizing the design of the skin-like optical device, we use the Monte Carlo method to investigate the light distribution after scattered and absorbed by a human tissue. Different parameters of light source and blood vessels are used to simulate the device and human tissue deformation respectively. The characteristics of the exited light are then summarized and analyzed to study the influence of the deformation. The simulation shows that the deformation of the device and human tissue will produce non-linear effects on the characteristics of the exited lights. Finally, we design and fabricate a skin-like device using the simulation results and use it to monitor photoplethysmogram signals. This work will aid in the design of skin-like optical devices in the future.
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Affiliation(s)
- Haicheng Li
- AML, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Changxing Zhang
- AML, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Xue Feng
- AML, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
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Yilmaz T, Foster R, Hao Y. Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels. Diagnostics (Basel) 2019; 9:diagnostics9010006. [PMID: 30626128 PMCID: PMC6468903 DOI: 10.3390/diagnostics9010006] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 12/13/2018] [Accepted: 12/21/2018] [Indexed: 12/13/2022] Open
Abstract
This paper reviews non-invasive blood glucose measurements via dielectric spectroscopy at microwave frequencies presented in the literature. The intent is to clarify the key challenges that must be overcome if this approach is to work, to suggest some possible ways towards addressing these challenges and to contribute towards prevention of unnecessary ‘reinvention of the wheel’.
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Affiliation(s)
- Tuba Yilmaz
- Department of Electronics and Communication Engineering, Istanbul Technical University, 34469 Istanbul, Turkey.
| | - Robert Foster
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK.
| | - Yang Hao
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
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