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Shen D, Wang J, Koncar V, Goyal K, Tao X. Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:4114. [PMID: 39000892 PMCID: PMC11244496 DOI: 10.3390/s24134114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/18/2024] [Accepted: 06/23/2024] [Indexed: 07/16/2024]
Abstract
This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.
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Affiliation(s)
- Deyao Shen
- College of Fashion and Design, Donghua University, Shanghai 200051, China
- École Nationale Supérieure des Arts et Industries Textiles-ENSAIT, ULR 2461-GEMTEX-Génie et Matériaux Textiles, University of Lille, F-59000 Lille, France
- Key Laboratory of Clothing Design and Technology, Donghua University, Ministry of Education, Shanghai 200051, China
| | - Jianping Wang
- College of Fashion and Design, Donghua University, Shanghai 200051, China
- Key Laboratory of Clothing Design and Technology, Donghua University, Ministry of Education, Shanghai 200051, China
- Shanghai Belt and Road Joint Laboratory of Textile Intelligent Manufacturing, Shanghai 200051, China
| | - Vladan Koncar
- École Nationale Supérieure des Arts et Industries Textiles-ENSAIT, ULR 2461-GEMTEX-Génie et Matériaux Textiles, University of Lille, F-59000 Lille, France
| | - Krittika Goyal
- Department of Manufacturing and Mechanical Engineering Technology, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Xuyuan Tao
- École Nationale Supérieure des Arts et Industries Textiles-ENSAIT, ULR 2461-GEMTEX-Génie et Matériaux Textiles, University of Lille, F-59000 Lille, France
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Liu Z, Xu X, Huang S, Huang X, Liu Z, Yao C, He M, Chen J, Chen HJ, Liu J, Xie X. Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals. MICROSYSTEMS & NANOENGINEERING 2024; 10:72. [PMID: 38828404 PMCID: PMC11143369 DOI: 10.1038/s41378-024-00702-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/18/2024] [Accepted: 04/10/2024] [Indexed: 06/05/2024]
Abstract
The collection of multiple-channel electrophysiological signals enables a comprehensive understanding of the spatial distribution and temporal features of electrophysiological activities. This approach can help to distinguish the traits and patterns of different ailments to enhance diagnostic accuracy. Microneedle array electrodes, which can penetrate skin without pain, can lessen the impedance between the electrodes and skin; however, current microneedle methods are limited to single channels and cannot achieve multichannel collection in small areas. Here, a multichannel (32 channels) microneedle dry electrode patch device was developed via a dimensionality reduction fabrication and integration approach and supported by a self-developed circuit system to record weak electrophysiological signals, including electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG) signals. The microneedles reduced the electrode-skin contact impedance by penetrating the nonconducting stratum corneum in a painless way. The multichannel microneedle array (MMA) enabled painless transdermal recording of multichannel electrophysiological signals from the subcutaneous space, with high temporal and spatial resolution, reaching the level of a single microneedle in terms of signal precision. The MMA demonstrated the detection of the spatial distribution of ECG, EMG and EEG signals in live rabbit models, and the microneedle electrode (MNE) achieved better signal quality in the transcutaneous detection of EEG signals than did the conventional flat dry electrode array. This work offers a promising opportunity to develop advanced tools for neural interface technology and electrophysiological recording.
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Grants
- National Key R&D Program of China (Grant No. 2021YFF1200700), the National Natural Science Foundation of China (Grant No. T2225010, 32171399, 32171456, 62105380), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515011267), the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (Grant No. 22dfx02), Pazhou Lab, Guangzhou (Grant No. PZL2021KF0003), the Opening Project of Key Laboratory of State Key Laboratory of Optoelectronic Materials and Technologies (OEMT-2022-ZRC-04), State key laboratory of precision measuring technology and instruments (Grant No. pilab2211),the Open Fund of the State Key Laboratory of Luminescent Materials and Devices (South China University of Technology, Grant No.2023-skllmd-09). the Open Fund of Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications (No. 2022A01), the Opening Project of State Key Laboratory of Bioelectronics, Southeast University (No. 2023-K09)
- China Postdoctoral Science Foundation
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Affiliation(s)
- Zhengjie Liu
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Xingyuan Xu
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Shuang Huang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Xinshuo Huang
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Zhibo Liu
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Chuanjie Yao
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Mengyi He
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Jiayi Chen
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Hui-jiuan Chen
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
| | - Jing Liu
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xi Xie
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
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Joutsen A, Cömert A, Kaappa E, Vanhatalo K, Riistama J, Vehkaoja A, Eskola H. ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts. Sci Rep 2024; 14:8882. [PMID: 38632263 PMCID: PMC11024137 DOI: 10.1038/s41598-024-56595-0] [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: 05/17/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact-resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin-electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin-electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric.
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Affiliation(s)
- Atte Joutsen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Finnish Cardiovascular Research Center, Tampere, Finland.
- Department of Medical Physics, Tampere University Hospital, Tampere, Finland.
| | - Alper Cömert
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Emma Kaappa
- Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
| | - Kirsi Vanhatalo
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center, Tampere, Finland
| | - Hannu Eskola
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Li H, Tan P, Rao Y, Bhattacharya S, Wang Z, Kim S, Gangopadhyay S, Shi H, Jankovic M, Huh H, Li Z, Maharjan P, Wells J, Jeong H, Jia Y, Lu N. E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin. Chem Rev 2024; 124:3220-3283. [PMID: 38465831 DOI: 10.1021/acs.chemrev.3c00626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.
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Affiliation(s)
- Hongbian Li
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Philip Tan
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yifan Rao
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sarnab Bhattacharya
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zheliang Wang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sangjun Kim
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Susmita Gangopadhyay
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Hongyang Shi
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Matija Jankovic
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Heeyong Huh
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhengjie Li
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Pukar Maharjan
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jonathan Wells
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Hyoyoung Jeong
- Department of Electrical and Computer Engineering, University of California Davis, Davis, California 95616, United States
| | - Yaoyao Jia
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [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: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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Sousa ASP, Noites A, Vilarinho R, Santos R. Long-Term Electrode-Skin Impedance Variation for Electromyographic Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:8582. [PMID: 37896675 PMCID: PMC10610867 DOI: 10.3390/s23208582] [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: 08/31/2023] [Revised: 09/21/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
This study aims to observe the evolution of the electrode-skin interface impedance of surface EMG electrodes over the time taken to determine the time of stabilization. Eight healthy subjects participated in the study. Electrode-skin impedance was evaluated in the rectus abdominal muscle every five minutes, over a total period of 50 min. A reduction of 13.23% in the impedance values was observed in minute 10 (p = 0.007), and a reduction of 9.02% was observed in minute 15 (p = 0.029). No statistically significant differences were observed in the other instants evaluated. The findings obtained in the present study demonstrate a decrease in electrode-skin impedance from minute 5 to minute 15, followed by a stabilization period with a low percentage of variation till minute 50.
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Affiliation(s)
- Andreia S. P. Sousa
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal; (A.N.); (R.V.); (R.S.)
| | - Andreia Noites
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal; (A.N.); (R.V.); (R.S.)
| | - Rui Vilarinho
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal; (A.N.); (R.V.); (R.S.)
- FP-I3ID, Escola Superior de Saúde-Fernando Pessoa, 4200-253 Porto, Portugal
| | - Rubim Santos
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal; (A.N.); (R.V.); (R.S.)
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Lueken M, Gramlich M, Leonhardt S, Marx N, Zink MD. Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation. SENSORS (BASEL, SWITZERLAND) 2023; 23:5618. [PMID: 37420786 DOI: 10.3390/s23125618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 07/09/2023]
Abstract
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.
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Affiliation(s)
- Markus Lueken
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Michael Gramlich
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
| | - Steffen Leonhardt
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Nikolaus Marx
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
| | - Matthias D Zink
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
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The Possible Role of Electrical Stimulation in Osteoporosis: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59010121. [PMID: 36676745 PMCID: PMC9861581 DOI: 10.3390/medicina59010121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023]
Abstract
Osteoporosis is mainly a geriatric disease with a high incidence, and the resulting spinal fractures and hip fractures cause great harm to patients. Anti-osteoporosis drugs are the main treatment for osteoporosis currently, but these drugs have potential clinical limitations and side effects, so the development of new therapies is of great significance to patients with osteoporosis. Electrical stimulation therapy mainly includes pulsed electromagnetic fields (PEMF), direct current (DC), and capacitive coupling (CC). Meanwhile, electrical stimulation therapy is clinically convenient without side effects. In recent years, many researchers have explored the use of electrical stimulation therapy for osteoporosis. Based on this, the role of electrical stimulation therapy in osteoporosis was summarized. In the future, electrical stimulation might become a new treatment for osteoporosis.
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Brehm PJ, Anderson AP. Modeling the Design Characteristics of Woven Textile Electrodes for long-Term ECG Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:598. [PMID: 36679395 PMCID: PMC9864099 DOI: 10.3390/s23020598] [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: 12/01/2022] [Revised: 12/25/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
An electrocardiograph records the periodic voltage generated by the heart over time. There is growing demand to continuously monitor the ECG for proactive health care and human performance optimization. To meet this demand, new conductive textile electrodes are being developed which offer an attractive alternative to adhesive gel electrodes but they come with their own challenges. The key challenge with textile electrodes is that the relationship between the manufacturing parameters and the ECG measurement is not well understood, making design an iterative process without the ability to prospectively develop woven electrodes with optimized performance. Here we address this challenge by applying the traditional skin-electrode interface circuit model to woven electrodes by constructing a parameterized model of the ECG system. Then the unknown parameters of the system are solved for with an iterative MATLAB optimizer using measured data captured with the woven electrodes. The results of this novel analysis confirm that yarn conductivity and total conductive area reduce skin electrode impedance. The results also indicate that electrode skin pressure and moisture require further investigation. By closing this gap in development, textile electrodes can be better designed and manufactured to meet the demands of long-term ECG capture.
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