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Journal articles on the topic 'Biosignal monitoring'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Klinger, Volkhard. "An IoT-Based Platform for Rehabilitation Monitoring and Biosignal Identification." International Journal of Privacy and Health Information Management 6, no. 1 (January 2018): 1–19. http://dx.doi.org/10.4018/ijphim.2018010101.

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This article describes how as a result of technological advances of the embedded system, the Internet-of-Things (IoT) has created a wealth of new applications and tailored solutions, even in the area of health and medical technology. The integration of state-of-the-art IoT-systems in an existing prototype platform for biosignal acquisition, identification, and prosthesis control provides new applications for prevention and rehabilitation monitoring. This article concentrates on an IoT-based platform for rehabilitation monitoring and biosignal identification. The IoT-characteristics for the application in the area of medical technology are discussed and the integration of such IoT-modules in the given architecture is introduced. Based on this extended architecture, new applications in the field of biosignal measurement, signal processing and biosignal monitoring are presented. Some results of a rehabilitation monitoring system, based on a self-designed IoT-module, integrated in the whole platform, are shown.
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2

Stuart, Tucker, Jessica Hanna, and Philipp Gutruf. "Wearable devices for continuous monitoring of biosignals: Challenges and opportunities." APL Bioengineering 6, no. 2 (June 1, 2022): 021502. http://dx.doi.org/10.1063/5.0086935.

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The ability for wearable devices to collect high-fidelity biosignals continuously over weeks and months at a time has become an increasingly sought-after characteristic to provide advanced diagnostic and therapeutic capabilities. Wearable devices for this purpose face a multitude of challenges such as formfactors with long-term user acceptance and power supplies that enable continuous operation without requiring extensive user interaction. This review summarizes design considerations associated with these attributes and summarizes recent advances toward continuous operation with high-fidelity biosignal recording abilities. The review also provides insight into systematic barriers for these device archetypes and outlines most promising technological approaches to expand capabilities. We conclude with a summary of current developments of hardware and approaches for embedded artificial intelligence in this wearable device class, which is pivotal for next generation autonomous diagnostic, therapeutic, and assistive health tools.
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Cogan, Diana, Javad Birjandtalab, Mehrdad Nourani, Jay Harvey, and Venkatesh Nagaraddi. "Multi-Biosignal Analysis for Epileptic Seizure Monitoring." International Journal of Neural Systems 27, no. 01 (November 8, 2016): 1650031. http://dx.doi.org/10.1142/s0129065716500313.

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Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient’s physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.
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Athavale, Yashodhan, and Sridhar Krishnan. "Biosignal monitoring using wearables: Observations and opportunities." Biomedical Signal Processing and Control 38 (September 2017): 22–33. http://dx.doi.org/10.1016/j.bspc.2017.03.011.

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Blachowicz, Tomasz, Guido Ehrmann, and Andrea Ehrmann. "Textile-Based Sensors for Biosignal Detection and Monitoring." Sensors 21, no. 18 (September 9, 2021): 6042. http://dx.doi.org/10.3390/s21186042.

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Biosignals often have to be detected in sports or for medical reasons. Typical biosignals are pulse and ECG (electrocardiogram), breathing, blood pressure, skin temperature, oxygen saturation, bioimpedance, etc. Typically, scientists attempt to measure these biosignals noninvasively, i.e., with electrodes or other sensors, detecting electric signals, measuring optical or chemical information. While short-time measurements or monitoring of patients in a hospital can be performed by systems based on common rigid electrodes, usually containing a large amount of wiring, long-term measurements on mobile patients or athletes necessitate other equipment. Here, textile-based sensors and textile-integrated data connections are preferred to avoid skin irritations and other unnecessary limitations of the monitored person. In this review, we give an overview of recent progress in textile-based electrodes for electrical measurements and new developments in textile-based chemical and other sensors for detection and monitoring of biosignals.
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6

Klinger, Volkhard. "SMoBAICS." International Journal of Privacy and Health Information Management 5, no. 2 (July 2017): 34–57. http://dx.doi.org/10.4018/ijphim.2017070103.

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Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.
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7

Shanmathi, N., and M. Jagannath. "Multimodal Biosignal Acquisition System for Remote Health Monitoring." Research Journal of Pharmacy and Technology 11, no. 12 (2018): 5265. http://dx.doi.org/10.5958/0974-360x.2018.00959.9.

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8

Mercado-Aguirre, Isabela M., Edgardo L. Mercado-Medina, Zulay D. Chavarro-Hernandez, Juan A. Dominguez-Jimenez, and Sonia H. Contreras-Ortiz. "A wearable system for biosignal monitoring in weightlifting." Sports Engineering 20, no. 1 (July 11, 2016): 73–80. http://dx.doi.org/10.1007/s12283-016-0212-z.

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9

Wu, Xianzhang, Zhangpeng Li, Honggang Wang, Jingxia Huang, Jinqing Wang, and Shengrong Yang. "Stretchable and self-healable electrical sensors with fingertip-like perception capability for surface texture discerning and biosignal monitoring." Journal of Materials Chemistry C 7, no. 29 (2019): 9008–17. http://dx.doi.org/10.1039/c9tc02575h.

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10

Murakami, D., and M. Makikawa. "Ambulatory Behavior Map, Physical Activity and Biosignal Monitoring System." Methods of Information in Medicine 36, no. 04/05 (October 1997): 360–63. http://dx.doi.org/10.1055/s-0038-1636848.

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Abstract:In this study, we have developed an ambulatory human behavior map and physical activity monitoring system. This was accomplished by equipping our portable digital biosignal memory device developed previously with GPS sensors and piezoresistive accelerometers. Using this new system, we can get a subject’s behavior map, and estimate his physical activities and posture changes in daily life.
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11

Oh, Hyeon Seok, Chung Hyeon Lee, Na Kyoung Kim, Taechang An, and Geon Hwee Kim. "Review: Sensors for Biosignal/Health Monitoring in Electronic Skin." Polymers 13, no. 15 (July 28, 2021): 2478. http://dx.doi.org/10.3390/polym13152478.

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Skin is the largest sensory organ and receives information from external stimuli. Human body signals have been monitored using wearable devices, which are gradually being replaced by electronic skin (E-skin). We assessed the basic technologies from two points of view: sensing mechanism and material. Firstly, E-skins were fabricated using a tactile sensor. Secondly, E-skin sensors were composed of an active component performing actual functions and a flexible component that served as a substrate. Based on the above fabrication processes, the technologies that need more development were introduced. All of these techniques, which achieve high performance in different ways, are covered briefly in this paper. We expect that patients’ quality of life can be improved by the application of E-skin devices, which represent an applied advanced technology for real-time bio- and health signal monitoring. The advanced E-skins are convenient and suitable to be applied in the fields of medicine, military and environmental monitoring.
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12

Beulah Devamalar, P. M., V. Thulasibai, G. Kavya, and T. Jayanandan. "Design and Implementation of Wearable Antenna for Biosignal Monitoring." Sensor Letters 14, no. 10 (October 1, 2016): 1014–18. http://dx.doi.org/10.1166/sl.2016.3552.

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13

Alyan, E. A., and S. A. Aljunid. "Development of wireless optical CDMA system for biosignal monitoring." Optik 145 (September 2017): 250–57. http://dx.doi.org/10.1016/j.ijleo.2017.07.053.

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14

Lee, Hyung-Bong, Sung-Wook Park, and Tae-Yun Chung. "Development of a Portable SpO2-based Biosignal Monitoring System." IEMEK Journal of Embedded Systems and Applications 8, no. 5 (October 31, 2013): 273–83. http://dx.doi.org/10.14372/iemek.2013.8.5.273.

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15

Wu, Xianzhang, Zhangpeng Li, Honggang Wang, Jingxia Huang, Jinqing Wang, and Shengrong Yang. "Correction: Stretchable and self-healable electrical sensors with fingertip-like perception capability for surface texture discerning and biosignal monitoring." Journal of Materials Chemistry C 7, no. 39 (2019): 12356. http://dx.doi.org/10.1039/c9tc90199j.

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Correction for ‘Stretchable and self-healable electrical sensors with fingertip-like perception capability for surface texture discerning and biosignal monitoring’ by Xianzhang Wu et al., J. Mater. Chem. C, 2019, 7, 9008–9017.
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16

Lee, Yechan, Sang-Gu Yim, Gyeong Won Lee, Sodam Kim, Hong Sung Kim, Dae Youn Hwang, Beum-Soo An, Jae Ho Lee, Sungbaek Seo, and Seung Yun Yang. "Self-Adherent Biodegradable Gelatin-Based Hydrogel Electrodes for Electrocardiography Monitoring." Sensors 20, no. 20 (October 9, 2020): 5737. http://dx.doi.org/10.3390/s20205737.

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Patch-type hydrogel electrodes have received increasing attention in biomedical applications due to their high biocompatibility and conformal adherence. However, their poor mechanical properties and non-uniform electrical performance in a large area of the hydrogel electrode should be improved for use in wearable devices for biosignal monitoring. Here, we developed self-adherent, biocompatible hydrogel electrodes composed of biodegradable gelatin and conductive polymers for electrocardiography (ECG) measurement. After incorporating conductive poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) into gelatin hydrogels crosslinked by natural crosslinkers (genipin), the mechanical properties and electrical conductivity of the hydrogel electrodes were improved and additionally optimized by adjusting the amounts of crosslinker and PEDOT:PSS, respectively. Furthermore, the effect of dimethyl sulfoxide, as a dopant, on the conductivity of hydrogels was investigated. The gelatin-based, conductive hydrogel patch displayed self-adherence to human skin with an adhesive strength of 0.85 N and achieved conformal contact with less skin irritation compared to conventional electrodes with a chemical adhesive layer. Eyelet-type hydrogel electrodes, which were compatible with conventional ECG measurement instruments, exhibited a comparable performance in 12-lead human ECG measurement with commercial ECG clinical electrodes (3M Red Dot). These self-adherent, biocompatible, gelatin-based hydrogel electrodes could be used for monitoring various biosignals, such as in electromyography and electroencephalography.
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17

Esposito, Daniele, Jessica Centracchio, Emilio Andreozzi, Gaetano D. Gargiulo, Ganesh R. Naik, and Paolo Bifulco. "Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey." Sensors 21, no. 20 (October 15, 2021): 6863. http://dx.doi.org/10.3390/s21206863.

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As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.
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18

Kuncoro, C. Bambang Dwi, Win-Jet Luo, and Yean-Der Kuan. "Wireless Photoplethysmography Sensor for Continuous Blood Pressure Biosignal Shape Acquisition." Journal of Sensors 2020 (February 24, 2020): 1–9. http://dx.doi.org/10.1155/2020/7192015.

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Blood pressure assessment plays a vital role in day-to-day clinical diagnosis procedures as well as personal monitoring. Thus, blood pressure monitoring devices must afford convenience and be easy to use with no side effects on the user. This paper presents a compact, economical, power-efficient, and convenient wireless plethysmography sensor for real-time blood pressure biosignal monitoring. The proposed sensor facilitates blood pressure signal shape sensing, signal conditioning, and data conversion as well as its wireless transmission to a monitoring terminal. Received data can, subsequently, be compiled and stored on a computer via a Wi-Fi module. During monitoring, users can observe blood pressure signals being processed and displayed on the graphical user interface (GUI)—developed using a virtual instrumentation (VI) application. The proposed device comprises a finger clip optical pulse sensor, analogue signal preprocessing, microcontroller, and Wi-Fi module. It consumes approximately 500 mW power when operating in the active mode and synthesized using commercial off-the-shelf (COTS) components. Experimental results reveal that the proposed device is reliable and facilitates efficient blood pressure monitoring. The proposed wireless photoplethysmographic (PPG) sensor is a preliminary (or first) version of the intended device manifestation. It provides raw blood pressure data for further classification. Additionally, the collected data concerning the blood pressure wave shape can be easily analysed for use in other biosignal observations, interpretations, and investigations. The design approach also allows the device to be built into a wearable system for further research purposes.
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19

Yoon, Dukyong, Sukhoon Lee, Tae Young Kim, JeongGil Ko, Wou Young Chung, and Rae Woong Park. "System for Collecting Biosignal Data from Multiple Patient Monitoring Systems." Healthcare Informatics Research 23, no. 4 (2017): 333. http://dx.doi.org/10.4258/hir.2017.23.4.333.

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20

Yuan, Ye, and Kebin Jia. "FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals." Sensors 19, no. 11 (May 28, 2019): 2429. http://dx.doi.org/10.3390/s19112429.

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Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various types of biomedical signals collected by different sensing channels provide different aspects of patient health information. However, due to the uncertainty and variability in clinical observation, not all the channels are relevant and important to the target task. Thus, in order to extract informative representations from multi-channel biosignals, channel awareness has become a key enabler for deep learning in biosignal processing and has attracted increasing research interest in health informatics. Towards this end, we propose FusionAtt—a deep fusional attention network that can learn channel-aware representations of multi-channel biosignals, while preserving complex correlations among all the channels. FusionAtt is able to dynamically quantify the importance of each biomedical channel, and relies on more informative ones to enhance feature representation in an end-to-end manner. We empirically evaluated FusionAtt in two clinical tasks: multi-channel seizure detection and multivariate sleep stage classification. Experimental results showed that FusionAtt consistently outperformed the state-of-the-art models in four different evaluation measurements, demonstrating the effectiveness of the proposed fusional attention mechanism.
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21

Cucchi, Matteo, Christopher Gruener, Lautaro Petrauskas, Peter Steiner, Hsin Tseng, Axel Fischer, Bogdan Penkovsky, et al. "Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification." Science Advances 7, no. 34 (August 2021): eabh0693. http://dx.doi.org/10.1126/sciadv.abh0693.

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Early detection of malign patterns in patients’ biological signals can save millions of lives. Despite the steady improvement of artificial intelligence–based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients’ data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow–power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues.
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Toral, Víctor, Antonio García, Francisco Romero, Diego Morales, Encarnación Castillo, Luis Parrilla, Francisco Gómez-Campos, Antonio Morillas, and Alejandro Sánchez. "Wearable System for Biosignal Acquisition and Monitoring Based on Reconfigurable Technologies." Sensors 19, no. 7 (April 2, 2019): 1590. http://dx.doi.org/10.3390/s19071590.

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Wearable monitoring devices are now a usual commodity in the market, especially for the monitoring of sports and physical activity. However, specialized wearable devices remain an open field for high-risk professionals, such as military personnel, fire and rescue, law enforcement, etc. In this work, a prototype wearable instrument, based on reconfigurable technologies and capable of monitoring electrocardiogram, oxygen saturation, and motion, is presented. This reconfigurable device allows a wide range of applications in conjunction with mobile devices. As a proof-of-concept, the reconfigurable instrument was been integrated into ad hoc glasses, in order to illustrate the non-invasive monitoring of the user. The performance of the presented prototype was validated against a commercial pulse oximeter, while several alternatives for QRS-complex detection were tested. For this type of scenario, clustering-based classification was found to be a very robust option.
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23

Korhonen, I., M. van Gils, A. Kari, and N. Saranummi. "Framework for Biosignal Interpretation in Intensive Care and Anesthesia." Methods of Information in Medicine 36, no. 04/05 (October 1997): 340–44. http://dx.doi.org/10.1055/s-0038-1636865.

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Abstract:Improved monitoring improves outcomes of care. As critical care is “critical”, everything that can be done to detect and prevent complications as early as possible benefits the patients. In spite of major efforts by the research community to develop and apply sophisticated biosignal interpretation methods (BSI), the uptake of the results by industry has been poor. Consequently, the BSI methods used in clinical routine are fairly simple. This paper postulates that the main reason for the poor uptake is the insufficient bridging between the actors (i.e., clinicians, industry and research). This makes it difficult for the BSI developers to understand what can be implemented into commercial systems and what will be accepted by clinicians as routine tools. A framework is suggested that enables improved interaction and cooperation between the actors. This framework is based on the emerging commercial patient monitoring and data management platforms which can be shared and utilized by all concerned, from research to development and finally to clinical evaluation.
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Lozano Montero, Karem, Mika-Matti Laurila, Mikko Peltokangas, Mira Haapala, Jarmo Verho, Niku Oksala, Antti Vehkaoja, and Matti Mäntysalo. "Self-Powered, Ultrathin, and Transparent Printed Pressure Sensor for Biosignal Monitoring." ACS Applied Electronic Materials 3, no. 10 (October 6, 2021): 4362–75. http://dx.doi.org/10.1021/acsaelm.1c00540.

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Lee, Seung-Youl, Sang-Hoon Park, Choon-Woo Lee, Hyun-Jun Kim, and Jae-Wook Chae. "Design and Implementation of Biosignal Monitoring for Enhancement of Soldier Survivability." Journal of the Korea Institute of Military Science and Technology 16, no. 6 (December 5, 2013): 841–46. http://dx.doi.org/10.9766/kimst.2013.16.6.841.

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26

Pawar, Pravin A., Damodar R. Edla, Thierry Edoh, Vijay Shinde, and Bert-Jan van Beijnum. "Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery." Interdisciplinary Sciences: Computational Life Sciences 11, no. 2 (October 31, 2017): 307–19. http://dx.doi.org/10.1007/s12539-017-0263-2.

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27

Cho, Hyun-Seung, Jin-Hee Yang, Sang-Min Kim, Jeong-Whan Lee, Hwi-Kuen Kwak, Je-Wook Chae, and Joo-Hyeon Lee. "Development of a Chest-Belt-Type Biosignal-Monitoring Wearable Platform System." Journal of Electrical Engineering & Technology 15, no. 4 (June 18, 2020): 1847–55. http://dx.doi.org/10.1007/s42835-020-00450-5.

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28

Nam, Sang Hun, Ji Yong Lee, and Jung Yoon Kim. "Biological-Signal-Based User-Interface System for Virtual-Reality Applications for Healthcare." Journal of Sensors 2018 (July 29, 2018): 1–10. http://dx.doi.org/10.1155/2018/9054758.

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Biosignal interfaces provide important data that reveal the physical status of a user, and they are used in the medical field for patient health status monitoring, medical automation, or rehabilitation services. Biosignals can be used in developing new contents, in conjunction with virtual reality, and are important factors for extracting user emotion or measuring user experience. A biological-signal-based user-interface system composed of sensor devices, a user-interface system, and an application that can extract biological-signal data from multiple biological-signal devices and be used by content developers was designed. A network-based protocol was used for unconstrained use of the device so that the biological signals can be freely received via USB, Bluetooth, WiFi, and an internal system module. A system that can extract biological-signal data from multiple biological-signal data and simultaneously extract and analyze the data from a virtual-reality-specific eye-tracking device was developed so that users who develop healthcare contents based on virtual-reality technology can easily use the biological signals.
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Hazer-Rau, Dilana, Lin Zhang, and Harald C. Traue. "A Workflow for Affective Computing and Stress Recognition from Biosignals." Engineering Proceedings 2, no. 1 (November 14, 2020): 85. http://dx.doi.org/10.3390/ecsa-7-08227.

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Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows the processing of biosignals in their raw state as obtained from wearable sensors. It consists of five stages: (1) Biosignal Preprocessing—raw data conversion and physiological data triggering, relevant information selection, artifact and noise filtering; (2) Feature Extraction—using different mathematical groups including amplitude, frequency, linearity, stationarity, entropy and variability, as well as cardiovascular-specific characteristics; (3) Feature Selection—dimension reduction and computation optimization using Forward Selection, Backward Elimination and Brute Force methods; (4) Affect Classification—machine learning using Support Vector Machine, Random Forest and k-Nearest Neighbor algorithms; (5) Model Validation—performance matrix computation using k-Cross, Leave-One-Subject-Out and Split Validations. All workflow stages are integrated into embedded functions and operators, allowing an automated execution of the recognition process. The next steps include further development of the algorithms and the integration of the developed tools into an easy-to-use system, thereby satisfying the needs of medical and psychological staff. Our automated workflow was evaluated using our uulmMAC database, previously developed for affective computing and machine learning applications in human–computer interaction.
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Jeong, Yu Ra, Geumbee Lee, Heun Park, and Jeong Sook Ha. "Stretchable, Skin-Attachable Electronics with Integrated Energy Storage Devices for Biosignal Monitoring." Accounts of Chemical Research 52, no. 1 (December 26, 2018): 91–99. http://dx.doi.org/10.1021/acs.accounts.8b00508.

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Fedorchuk, Maksym M., and Anton Oleksandrovych Popov. "Techniques and Methods for Biosignal Analysis for Monitoring the Depth of Anesthesia." Microsystems, Electronics and Acoustics 23, no. 4 (August 31, 2018): 12–21. http://dx.doi.org/10.20535/2523-4455.2018.23.3.125236.

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Sekitani, Tsuyoshi, Shusuke Yoshimoto, Teppei Araki, Yuki Noda, and Takafumi Uemura. "9-2: Invited Paper: Stretchable Electronics for Wearable Microvolt Biosignal Monitoring Systems." SID Symposium Digest of Technical Papers 49, no. 1 (May 2018): 84–86. http://dx.doi.org/10.1002/sdtp.12564.

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33

Nakayama, Fernando, Paulo Lenz, Stella Banou, Michele Nogueira, Aldri Santos, and Kaushik R. Chowdhury. "A Continuous User Authentication System Based on Galvanic Coupling Communication for s-Health." Wireless Communications and Mobile Computing 2019 (November 28, 2019): 1–11. http://dx.doi.org/10.1155/2019/9361017.

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Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.
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Sadri, Behnam, Alberto Miralles Abete, and Ramses V. Martinez. "Simultaneous electrophysiological recording and self-powered biosignal monitoring using epidermal, nanotexturized, triboelectronic devices." Nanotechnology 30, no. 27 (April 17, 2019): 274003. http://dx.doi.org/10.1088/1361-6528/ab10e9.

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Ganapathy, Nagarajan, Ramakrishnan Swaminathan, and Thomas Deserno. "Deep Learning on 1-D Biosignals: a Taxonomy-based Survey." Yearbook of Medical Informatics 27, no. 01 (August 2018): 098–109. http://dx.doi.org/10.1055/s-0038-1667083.

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Objectives: Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field. Methods: A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model. Results: Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography, photoplethysmography, electrooculography, continuous glucose monitoring, acoustic respiratory signal, blood pressure, and electrodermal activity signal, while ballistocardiography or seismocardiography have yet to be analyzed using deep learning techniques. In supervised and unsupervised applications, CNNs and restricted Boltzmann machines are the most and least frequently used, (n = 34) and (n = 15), respectively. Conclusion: Our key-code classification of relevant papers was used to cluster the approaches that have been published to date and demonstrated a large variability of research with respect to data, application, and network topology. Future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness. Furthermore, application-driven approaches and updated training data from mobile recordings are needed.
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Zhu, Zhu, Peixian Wang, and Fuguang Wang. "Design of Health Detection System for Elderly Smart Watch Based on Biosignal Acquisition." Journal of Sensors 2022 (July 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/6988001.

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The health monitoring of the elderly has attracted increasing attention of researchers. Based on the biosignal acquisition method, this paper proposes a design structure of the health detection system for smart watches for the elderly and realizes the effective detection of health signals by analyzing the Lipschitz exponent of the maximum value column of the transform. The multiphysiological parameter acquisition and monitoring system of the wearable smart watch designed in this paper can continuously monitor the physiological parameters of the elderly such as body temperature, pulse, and respiration for a long time and solve the problem of the accuracy of the health detection of the elderly. In the simulation process, based on the performance of the synchronization source and the difference of the network path, the model applies the multivariate and multiscale biological signals to collect the human gait acceleration. The experimental results show that, compared with the international recognition rate obtained for this data set, the highest recognition rate obtained by the method in this paper reaches 96.5%, which can provide a calibration accuracy of 1 ~ 50 ms, and the synchronized system time and the national time service center network are given. The error obtained by comparing the published time is within 50 ms, which meets the accuracy requirements of the time protocol. The results fully prove that the algorithm in this paper can effectively extract the biosignal features of the elderly’s health detection and has good statistical features and classification accuracy.
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Hong, Seunghyeok, Jeong Heo, and Kwang Suk Park. "Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare." Sensors 21, no. 22 (November 14, 2021): 7564. http://dx.doi.org/10.3390/s21227564.

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We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.
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Tomasini, Marco, Simone Benatti, Bojan Milosevic, Elisabetta Farella, and Luca Benini. "Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices." IEEE Sensors Journal 16, no. 10 (May 2016): 3887–95. http://dx.doi.org/10.1109/jsen.2016.2536363.

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Lin, Chin-Teng, Chen-Yu Wang, Kuan-Chih Huang, Shi-Jinn Horng, and Lun-De Liao. "Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications." Emergency Medicine International 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9954669.

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For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.
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LEE, JIANN-DER, SHU-YEN WAN, and IN-KAI HUNG. "DESIGN AND IMPLEMENTATION OF A PC-BASED MULTIMEDIA BIOSIGNAL INTEGRATION SYSTEM." Biomedical Engineering: Applications, Basis and Communications 13, no. 06 (December 25, 2001): 267–75. http://dx.doi.org/10.4015/s1016237201000340.

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This paper presents a novel framework to provide the useful models such as Teleconsultation and Telemonitoring for PACS (Picture Archiving and Communication System). It mainly makes use of multimedia together with network-transferring technologies, etc. to integrate the various departments in the hospital, with the expectation of helping the specialists make consultation and share the resources through the network as well as reducing the management cost; so as to enhance efficiency, improve medical quality and provide the patients with better service. The whole system in this approach is based on the most popular platform of Windows and builds the medical database by PACS database programming. It combines OLE DB interface and ADO technology, etc. to provide the users with operations such as data search and access; and makes use of the network construction; data transfer and message transfer of DirectPlay object throughout the whole system to operate. Besides providing general text communication, the system also provides the video-conferencing to enhance the interaction among people. Finally, in order to benefit the network transfer, the system also provides lossless data compression service to increase data-transferring speed. This paper also proposes the Group Data Provider Selection (GDPS) mechanism, by which the system not only can reduce the system-management workload of the database, but also can find out the best network-transferring path automatically to transfer the data to the destination. Moreover, in the experiment of the Telemonitoring module, under the condition that there are three signal channels for each biosignal extractor and the sampling frequency of each channel is set up at 500Hz, the nursing monitoring station can monitor over 12 biosignal extractors at the same time, which has reached the requirement of a practical PACS used in modern hospital. This also illustrates the effectiveness and the potential of the proposed scheme.
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Kortelainen, J. M., J. Pärkkä, M. Tenhunen, S. L. Himanen, A. M. Bianchi, and M. Migliorini. "Monitoring Nocturnal Heart Rate with Bed Sensor." Methods of Information in Medicine 53, no. 04 (2014): 308–13. http://dx.doi.org/10.3414/me13-02-0053.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG).Methods: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection.Results: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected.Conclusions: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.
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Andreozzi, Emilio, Jessica Centracchio, Vincenzo Punzo, Daniele Esposito, Caitlin Polley, Gaetano D. Gargiulo, and Paolo Bifulco. "Respiration Monitoring via Forcecardiography Sensors." Sensors 21, no. 12 (June 9, 2021): 3996. http://dx.doi.org/10.3390/s21123996.

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In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.
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Antonopoulos, Christos P., and Nikolaos S. Voros. "A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms." Electronics 6, no. 3 (July 31, 2017): 54. http://dx.doi.org/10.3390/electronics6030054.

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von Luhmann, Alexander, Heidrun Wabnitz, Tilmann Sander, and Klaus-Robert Muller. "M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring." IEEE Transactions on Biomedical Engineering 64, no. 6 (June 2017): 1199–210. http://dx.doi.org/10.1109/tbme.2016.2594127.

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45

Chun, Sungwoo, Da Wan Kim, Sangyul Baik, Heon Joon Lee, Jung Heon Lee, Suk Ho Bhang, and Changhyun Pang. "Conductive and Stretchable Adhesive Electronics with Miniaturized Octopus-Like Suckers against Dry/Wet Skin for Biosignal Monitoring." Advanced Functional Materials 28, no. 52 (November 9, 2018): 1805224. http://dx.doi.org/10.1002/adfm.201805224.

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46

Min, Lingtong, Qinyi Lv, Laisen Nie, and Deyun Zhou. "Anti-Interference Heartbeat Measurement Based on a Miniaturized Doppler Radar Sensor." Advances in Mathematical Physics 2021 (August 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/1620938.

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It is a hot topic to utilize the Doppler radar sensor in noncontact biosignal monitoring nowadays. Unfortunately, most detections are easily affected by interference or strong noise. Even slight body movements can cause serious demodulation distortion. In this paper, we proposed a novel algorithm to solve the sudden and unexpected interference. Firstly, the one-dimensional signal detected by the sensor is divided into segments to form a two-dimensional data matrix. In both the intrasegment and intersegment domains of the data matrix, a robust algorithm is employed to suppress unwanted interference, which significantly improves the robustness of demodulation. Experiments show the effectiveness of the proposed algorithm, based on which weak heartbeat signal hidden in the interference can be well extracted.
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47

Xiao, Xueliang, Sandeep Pirbhulal, Ke Dong, Wanqing Wu, and Xi Mei. "Performance Evaluation of Plain Weave and Honeycomb Weave Electrodes for Human ECG Monitoring." Journal of Sensors 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7539840.

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Long-time monitoring of physiological parameters can scrutinize human health conditions so as to use electrocardiogram (ECG) for diagnosis of some human’s chronic cardiovascular diseases. The continuous monitoring requires the wearable electrodes to be breathable, flexible, biocompatible, and skin-affinity friendly. Weave electrodes are innovative materials to supply these potential performances. In this paper, four conductive weave electrodes in plain and honeycomb weave patterns were developed to monitor human ECG signals. A wearable belt platform was developed to mount such electrodes for acquisition of ECG signals using a back-end electronic circuit and a signal transfer unit. The performance of weave ECG electrodes was evaluated in terms of skin-electrode contacting impedance, comfortability, ECG electrical characteristics, and signal fidelity. Such performances were then compared with the values from Ag/AgCl reference electrode. The test results showed that lower skin-electrode impedance, higher R-peak amplitude, and signal-to-noise ratio (SNR) value were obtained with the increased density of conductive filaments in weave and honeycomb weave electrode presented higher comfort but poorer signal quality of ECG. This study inspires an acceptable way of weave electrodes in long- and real-time of human biosignal monitoring.
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Wang, Yan, Sunghoon Lee, Haoyang Wang, Zhi Jiang, Yasutoshi Jimbo, Chunya Wang, Binghao Wang, et al. "Robust, self-adhesive, reinforced polymeric nanofilms enabling gas-permeable dry electrodes for long-term application." Proceedings of the National Academy of Sciences 118, no. 38 (September 13, 2021): e2111904118. http://dx.doi.org/10.1073/pnas.2111904118.

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Robust polymeric nanofilms can be used to construct gas-permeable soft electronics that can directly adhere to soft biological tissue for continuous, long-term biosignal monitoring. However, it is challenging to fabricate gas-permeable dry electrodes that can self-adhere to the human skin and retain their functionality for long-term (>1 d) health monitoring. We have succeeded in developing an extraordinarily robust, self-adhesive, gas-permeable nanofilm with a thickness of only 95 nm. It exhibits an extremely high skin adhesion energy per unit area of 159 μJ/cm2. The nanofilm can self-adhere to the human skin by van der Waals forces alone, for 1 wk, without any adhesive materials or tapes. The nanofilm is ultradurable, and it can support liquids that are 79,000 times heavier than its own weight with a tensile stress of 7.82 MPa. The advantageous features of its thinness, self-adhesiveness, and robustness enable a gas-permeable dry electrode comprising of a nanofilm and an Au layer, resulting in a continuous monitoring of electrocardiogram signals with a high signal-to-noise ratio (34 dB) for 1 wk.
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Pillai, Sangeeth, Akshaya Upadhyay, Darren Sayson, Bich Hong Nguyen, and Simon D. Tran. "Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring." Molecules 27, no. 1 (December 28, 2021): 165. http://dx.doi.org/10.3390/molecules27010165.

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In the past decade, wearable biosensors have radically changed our outlook on contemporary medical healthcare monitoring systems. These smart, multiplexed devices allow us to quantify dynamic biological signals in real time through highly sensitive, miniaturized sensing platforms, thereby decentralizing the concept of regular clinical check-ups and diagnosis towards more versatile, remote, and personalized healthcare monitoring. This paradigm shift in healthcare delivery can be attributed to the development of nanomaterials and improvements made to non-invasive biosignal detection systems alongside integrated approaches for multifaceted data acquisition and interpretation. The discovery of new biomarkers and the use of bioaffinity recognition elements like aptamers and peptide arrays combined with the use of newly developed, flexible, and conductive materials that interact with skin surfaces has led to the widespread application of biosensors in the biomedical field. This review focuses on the recent advances made in wearable technology for remote healthcare monitoring. It classifies their development and application in terms of electrochemical, mechanical, and optical modes of transduction and type of material used and discusses the shortcomings accompanying their large-scale fabrication and commercialization. A brief note on the most widely used materials and their improvements in wearable sensor development is outlined along with instructions for the future of medical wearables.
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Bednar, Tadeas, Branko Babusiak, Michal Labuda, Milan Smetana, and Stefan Borik. "Common-Mode Voltage Reduction in Capacitive Sensing of Biosignal Using Capacitive Grounding and DRL Electrode." Sensors 21, no. 7 (April 6, 2021): 2568. http://dx.doi.org/10.3390/s21072568.

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A capacitive measurement of the biosignals is a very comfortable and unobtrusive way suitable for long-term and wearable monitoring of health conditions. This type of sensing is very susceptible to noise from the surroundings. One of the main noise sources is power-line noise, which acts as a common-mode voltage at the input terminals of the acquisition unit. The origin and methods of noise reduction are described on electric models. Two methods of noise removal are modeled and experimentally verified in the paper. The first method uses a passive capacitive grounding electrode, and the second uses an active capacitive Driven Right Leg (DRL) electrode. The effect of grounding electrode size on noise suppression is experimentally investigated. The increasing electrode area reduces power-line noise: the power of power-line frequency within the measured signal is 70.96 dB, 59.13 dB, and 43.44 dB for a grounding electrode area of 1650 cm2, 3300 cm2, and 4950 cm2, respectively. The capacitive DRL electrode shows better efficiency in common-mode noise rejection than the grounding electrode. When using an electrode area of 1650 cm2, the DRL achieved 46.3 dB better attenuation than the grounding electrode at power-line frequency. In contrast to the grounding electrode, the DRL electrode reduces a capacitive measurement system’s financial costs due to the smaller electrode area made of the costly conductive textile.
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