Relevant bibliographies by topics / Biosignal monitoring

Academic literature on the topic 'Biosignal monitoring'

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

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Journal articles on the topic "Biosignal monitoring"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Biosignal monitoring"

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Snäll, Jonatan. "Software development of Biosignal Pi : An affordable open source platform for monitoring ECG and respiration." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154211.

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In order to handle the increasing costs of healthcare more of the care and monitoring will take place in the patient’s home. It is therefore desirable to develop smaller and portable systems that can record important biosignals such as the electrical activity of the heart in the form of an ECG. This project is a continuation on a previous project that developed a shield that can be connected to the GPIO pins of a Raspberry Pi, a credit-card sized computer. The shield contains an ADAS1000, a low power and compact device that can record the electrical activity of the heart along with respiration. The aim of this project was to develop an application that can run on the Raspberry Pi in order to display the captured data from the shield on a screen along with storing the data for further processing. The project was successful in the way that the requirements for the software have been fulfilled.
För att hantera den ökande kostnaden för hälso- och sjukvård kommer en större del av övervakning samt vård att ske i patientens hem. Det kommer därför att vara önskvärt att utveckla mindre system som är lättare att hantera än de större traditionella apparaterna för att samla in vanliga biosignaler som exempelvis ett EKG. Detta projekt är en fortsättning på ett tidigare projekt vars syfte var att framställa en ”sköld” som kan kopplas ihop med en Raspberry Pi via dess GPIO pinnar. Det föregående projektet var lyckat och en sköld innehållande en ADAS1000 som kan samla in bl.a. ett EKG samt andningen framställdes. Syftet med detta projekt var att utveckla en applikation som kan köras på en Raspberry Pi och på så sätt visa den data som samlas in från skölden på en skärm. Det skulle även vara möjligt att spara insamlad data för senare användning. Projektet resulterade i en applikation som uppfyllde dessa krav.
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Gomes, Ricardo Rafael Baptista. "Long-term biosignals visualization and processing." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/7979.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
Long-term biosignals acquisitions are an important source of information about the patients’state and its evolution. However, long-term biosignals monitoring involves managing extremely large datasets, which makes signal visualization and processing a complex task. To overcome these problems, a new data structure to manage long-term biosignals was developed. Based on this new data structure, dedicated tools for long-term biosignals visualization and processing were implemented. A multilevel visualization tool for any type of biosignals, based on subsampling is presented, focused on four representative signal parameters (mean, maximum, minimum and standard deviation error). The visualization tool enables an overview of the entire signal and a more detailed visualization in specific parts which we want to highlight, allowing an user friendly interaction that leads to an easier signal exploring. The ”map” and ”reduce” concept is also exposed for long-term biosignal processing. A processing tool (ECG peak detection) was adapted for long-term biosignals. In order to test the developed algorithm, long-term biosignals acquisitions (approximately 8 hours each) were carried out. The visualization tool has proven to be faster than the standard methods, allowing a fast navigation over the different visualization levels of biosignals. Regarding the developed processing algorithm, it detected the peaks of long-term ECG signals with fewer time consuming than the nonparalell processing algorithm. The non-specific characteristics of the new data structure, visualization tool and the speed improvement in signal processing introduced by these algorithms makes them powerful tools for long-term biosignals visualization and processing.
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Eguchi, Kana. "Easy-to-Use Biosignal Monitoring: Wearable Device for Muscle Activity Measurement during Sleep in Daily Life." Doctoral thesis, Kyoto University, 2020. http://hdl.handle.net/2433/253414.

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京都大学
0048
新制・課程博士
博士(情報学)
甲第22578号
情博第715号
新制||情||123(附属図書館)
京都大学大学院情報学研究科社会情報学専攻
(主査)教授 黒田 知宏, 教授 守屋 和幸, 教授 吉川 正俊
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
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Radüntz, Thea. "Biophysiological Mental-State Monitoring during Human-Computer Interaction." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23026.

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Die langfristigen Folgen von psychischer Fehlbeanspruchung stellen ein beträchtliches Problem unserer modernen Gesellschaft dar. Zur Identifizierung derartiger Fehlbelastungen während der Mensch-Maschine-Interaktion (MMI) kann die objektive, kontinuierliche Messung der psychischen Beanspruchung einen wesentlichen Beitrag leisten. Neueste Entwicklungen in der Sensortechnologie und der algorithmischen Methodenentwicklung auf Basis von KI liefern die Grundlagen zu ihrer messtechnischen Bestimmung. Vorarbeiten zur Entwicklung einer Methode zur neuronalen Beanspruchungsdiagnostik sind bereits erfolgt (Radüntz, 2017). Eine praxisrelevante Nutzung dieser Ergebnisse ist erfolgsversprechend, wenn die Methode mit Wearables kombiniert werden kann. Gleichzeitig sind die Evaluation und bedingungsbezogene Reliabilitätsprüfung der entwickelten Methode zur neuronalen Beanspruchungsdiagnostik in realitätsnahen Umgebungen erforderlich. Im Rahmen von experimentellen Untersuchungen der Gebrauchstauglichkeit von kommerziellen EEG-Registrierungssystemen für den mobilen Feldeinsatz wird die darauf basierende Systemauswahl für die MMI-Praxis getroffen. Die Untersuchungen zur Validierung der kontinuierlichen Methode zur Beanspruchungsdetektion erfolgt am Beispiel des Fluglotsenarbeitsplatzes beim simulierten „Arrival Management“.
The long-term negative consequences of inappropriate mental workload on employee health constitute a serious problem for a digitalized society. Continuous, objective assessment of mental workload can provide an essential contribution to the identification of such improper load. Recent improvements in sensor technology and algorithmic methods for biosignal processing are the basis for the quantitative determination of mental workload. Neuronal workload measurement has the advantage that workload registration is located directly there where human information processing takes place, namely the brain. Preliminary studies for the development of a method for neuronal workload registration by use of the electroencephalogram (EEG) have already been carried out [Rad16, Rad17]. For the field use of these findings, the mental workload assess- ment on the basis of the EEG must be evaluated and its reliability examined with respect to several conditions in realistic environments. A further essential require-ment is that the method can be combined with the innovative technologies of gel free EEG registration and wireless signal transmission. Hence, the presented papers include two investigations. Main subject of the first investigation are experimental studies on the usability of commercially-oriented EEG systems for mobile field use and system selection for the future work. Main subject of the second investigation is the evaluation of the continuous method for neuronal mental workload registration in the field. Thereby, a challenging application was used, namely the arrival management of aircraft. The simulation of the air traffic control environment allows the realisation of realistic conditions with different levels of task load. Furthermore, the work is well contextualized in a domain which is very sensible to human-factors research.
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Parmakis, Gerry. "Artificial pattern recognition with indeterminate input : a methodological review, and an original design for an adaptive system having prospective application in the real-time monitoring of biosignals such as ECG." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310728.

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Sarmento, Ana Sofia Nascimento. "Improving skin conductivity to small ions for enhanced biosignal monitoring." Master's thesis, 2018. https://hdl.handle.net/10216/114359.

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Sarmento, Ana Sofia Nascimento. "Improving skin conductivity to small ions for enhanced biosignal monitoring." Dissertação, 2018. https://hdl.handle.net/10216/114359.

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Sarmento, Ana Sofia Nascimento. "Improving skin conductivity to small ions for enhanced biosignal monitoring." Dissertação, 2002. https://repositorio-aberto.up.pt/handle/10216/114359.

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Saleh, Abdulelah. "Inkjet Printing of a Two-Dimensional Conductor for Cutaneous Biosignal Monitoring." Thesis, 2019. http://hdl.handle.net/10754/652930.

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Wearables for health monitoring are rapidly advancing as evidenced by the number of wearable products on the market. More recently, the US Food and Drug Administration approved the Apple Watch for heart monitoring, indicating that wearables are going to be a part of our lives sooner than expected. However, wearables are still based on rigid, conventional electronic materials and fabrication procedures. The use of flexible conducting materials fabricated on flexible substrates allows for more comprehensive health monitoring because of the seamless integration and conformability of such devices with the human skin. Many materials can be used to fabricate flexible electronics such as thin metals, liquid metals, conducting polymers, and 1D and 2D materials. Ti3C2 MXene is a promising 2D material that shows flexibility as well as desirable electronic properties. Ti3C2 MXene is easily processable in aqueous solutions and can be an excellent functional ink for inkjet printing. Here we report the fabrication and the properties of Ti3C2 MXene films inkjet-printed from aqueous dispersions with a nonionic surfactant. The films are uniform and formed with only a few layers on glass and tattoo paper. The MXene films printed on tattoo are used to record ECG signals with comparable signal-to-noise ratio to commercial Ag/AgCl electrodes despite the absence of gels to lower skin-contact impedance. Due to their high charge storage capacity and mixed (ionic and electronic) conductivity, inkjet-printed MXene films open up a new avenue for applications beyond health monitoring.
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Moura, André Magalhães. "Construction of a biosignal measurement device and its dashboard for swimming training." Master's thesis, 2021. http://hdl.handle.net/10400.6/12025.

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This research work draws on previous experimental research and aims to further develop and refine a biosignal measurement device that will allow the capture of a swimmer’s biosignals via Bluetooth. It also encompasses the construction of a dashboard that will allow a swimming instructor or coach to monitor and improve the athletes’ swimming practice.
O âmbito deste trabalho incide no desenvolvimento/criação de um dispositivo de medição de biosinais (acelerometria, eletromiografia e eletrocardiografia) para que através de uma conexão via Bluetooth seja feita a captura destes valores num nadador. Abrange ainda o desenvolvimento de um dashboard que permite ao treinador acompanhar e melhorar os treinos de um atleta.
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Books on the topic "Biosignal monitoring"

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Amine, Naït-Ali, ed. Advanced biosignal processing. Berlin: Springer, 2009.

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Urban, Gerald A., Roland Fried, Sven Bode, Gerald Czygan, and Martin Daumer. Biosignale und Monitoring. De Gruyter, Inc., 2021.

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Guttmann, Joseph, Roland Fried, Sven Bode, Gerald Czygan, and Martin Daumer. Biosignale und Monitoring. de Gruyter GmbH, Walter, 2030.

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Urban, Gerald A., Thomas Penzel, and Jens Haueisen. Biosignale und Monitoring. de Gruyter GmbH, Walter, 2030.

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Book chapters on the topic "Biosignal monitoring"

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Sengupta, Joydeep, Nupur Baviskar, and Surbhi Shukla. "Biosignal Acquisition System for Stress Monitoring." In Mobile Communication and Power Engineering, 451–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35864-7_69.

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Ritika Saxena, Sushabhan Choudhary, Rajesh Singh, and Anshuman Prakash. "Biosignal Acquisition of Stress Monitoring Through Wearable Device." In Proceeding of International Conference on Intelligent Communication, Control and Devices, 803–9. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1708-7_93.

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Stan, A., R. Lupu, M. Ciorap, and R. Ciorap. "Biosignal Monitoring and Processing for Management of Hypertension." In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, 537–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13039-7_135.

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Lovell, Nigel H., and Stephen J. Redmond. "Biosignal Processing to Meet the Emerging Needs of Telehealth Monitoring Environments." In Lecture Notes in Electrical Engineering, 263–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05167-8_15.

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Silva, Hugo, Susana Palma, and Hugo Gamboa. "AAL+: Continuous Institutional and Home Care Through Wireless Biosignal Monitoring Systems." In Handbook of Digital Homecare, 115–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/8754_2011_25.

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Pannerselvam, Ithayarani. "A Multimodal Biosignal Compression Technique for Monitoring Health in Wearable Devices." In Artificial Intelligence and Cybersecurity, 155–74. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097518-10.

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Valenza, Gaetano, and Enzo Pasquale Scilingo. "Exploiting Physiological Sensors and Biosignal Processing to Enhance Monitoring Care in Mental Health." In Handbook of Large-Scale Distributed Computing in Smart Healthcare, 515–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58280-1_19.

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López-de-Ipiña, K., J. Solé-Casals, U. Martinez de Lizarduy, P. M. Calvo, J. Iradi, M. Faundez-Zanuy, and A. Bergareche. "Non-invasive Biosignal Analysis Oriented to Early Diagnosis and Monitoring of Cognitive Impairments." In Converging Clinical and Engineering Research on Neurorehabilitation II, 867–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46669-9_141.

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Golz, Martin, and David Sommer. "Automatic Knowledge Extraction: Fusion of Human Expert Ratings and Biosignal Features for Fatigue Monitoring Applications." In Signal Processing Techniques for Knowledge Extraction and Information Fusion, 299–316. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7_16.

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Kim, Young-Hyuk, Il-Kown Lim, Jae-Pil Lee, Jae-Gwang Lee, and Jae-Kwang Lee. "Study on Low-Power Transmission Protocols for ZigBee Wireless Network-Based Remote Biosignal Monitoring Systems." In Lecture Notes in Electrical Engineering, 709–16. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5857-5_76.

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Conference papers on the topic "Biosignal monitoring"

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Perego, Paolo, Giuseppe Andreoni, Rinaldo Zanini, and Roberto Bellù. "Wearable Biosignal Monitoring System for Newborns." In 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies". ICST, 2014. http://dx.doi.org/10.4108/icst.mobihealth.2014.257403.

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Sakaue, Yusuke, and Masaaki Makikawa. "Development of Wireless Biosignal Monitoring Device." In 6th International Special Topic Conference on Information Technology Applications in Biomedicine, 2007. IEEE, 2007. http://dx.doi.org/10.1109/itab.2007.4407409.

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Dappuri, Bhasker, M. Tamilselvi, E. Baburaj, and S. Omkumar. "Biosignal electrode based patient health monitoring system." In INDUSTRIAL, MECHANICAL AND ELECTRICAL ENGINEERING. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0109775.

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Owyeung, Rachel, Wenxin Zeng, and Sameer Sonkusale. "Eutectogel Electrodes for Long-Term Biosignal Monitoring." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.9967321.

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Jung, Hachul, Dahye Kwon, Seunga Lee, Jinwoo Ahn, A.-Hee Kim, Young-Jin Kim, and Jinhee Moon. "Carbon Based Electrode for Wearable Biosignal Monitoring Patch." In 2018 International Flexible Electronics Technology Conference (IFETC). IEEE, 2018. http://dx.doi.org/10.1109/ifetc.2018.8583893.

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Gupta, Yashi, Suman Kumar, and Vijay Mago. "Pregnancy Health Monitoring System based on Biosignal Analysis." In 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2019. http://dx.doi.org/10.1109/tsp.2019.8769074.

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Singh, Rajiv Ranjan, Sailesh Conjeti, and Rahul Banerjee. "Biosignal based on-road stress monitoring for automotive drivers." In 2012 National Conference on Communications (NCC). IEEE, 2012. http://dx.doi.org/10.1109/ncc.2012.6176845.

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Lozano, Jorge, and Boris Stoeber. "Microspike Array Electrode with Flexible Backing for Biosignal Monitoring." In 2019 IEEE SENSORS. IEEE, 2019. http://dx.doi.org/10.1109/sensors43011.2019.8956869.

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Chen, Pei-Jarn, Jui-Yu Weng, and Yi-Kai Chiou. "Implement an IOT Platform of Biosignal Monitoring for Nurse Station." In 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2020. http://dx.doi.org/10.1109/ecbios50299.2020.9203686.

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Saisho, Osamu, Keiichiro Kashiwagi, Yui Saito, and Tomoyuki Fujino. "Adaptive biosignal data gathering for distributed and continual remote monitoring." In UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3410530.3414413.

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