Relevant bibliographies by topics / Monitoring physiological data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Monitoring physiological data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Monitoring physiological data"

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Scharf, Tom D. "Auditory alarms for physiological data monitoring." Journal of the Acoustical Society of America 119, no. 3 (2006): 1311. http://dx.doi.org/10.1121/1.2185068.

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Tambe, Sagar B., Ravindra C. Thool, and Vijaya R. Thool. "Cluster Based Wireless Mobile Healthcare System for Physiological Data Monitoring." Procedia Computer Science 78 (2016): 40–47. http://dx.doi.org/10.1016/j.procs.2016.02.008.

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Dash, Jonathan, Grant Pearse, and Michael Watt. "UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health." Remote Sensing 10, no. 8 (2018): 1216. http://dx.doi.org/10.3390/rs10081216.

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The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a careful
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Ebner-Priemer, Ulrich W., and Thomas Kubiak. "Psychological and Psychophysiological Ambulatory Monitoring." European Journal of Psychological Assessment 23, no. 4 (2007): 214–26. http://dx.doi.org/10.1027/1015-5759.23.4.214.

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Abstract. Ambulatory assessment targets capturing psychological, behavioral, and physiological data in “real time” using in-field data acquisition systems. Although ambulatory assessment research has flourished particularly in the last decades, overviews on hardware and software solutions for monitoring are scarce, and - if found - are often outdated. In this review, we give an overview of current software and hardware solutions, focusing on multichannel systems for physiological data acquisition and hand-held computer based “experience sampling” systems. We aim at offering the reader guidance
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Stacey, Michael John, N. Hill, and D. Woods. "Physiological monitoring for healthy military personnel." Journal of the Royal Army Medical Corps 164, no. 4 (2017): 290–92. http://dx.doi.org/10.1136/jramc-2017-000851.

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Military employment commonly exposes personnel to strenuous physical exertion. The resulting interaction between occupational stress and individual susceptibility to illness demands careful management. This could extend to prospective identification of high physiological strain in healthy personnel, in addition to recognition and protection of vulnerable individuals. The emergence and ubiquitous uptake of ‘wearable’ physiological and medical monitoring devices might help to address this challenge, but requires that the right questions are asked in sourcing, developing, validating and applying
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R. Geers, J. Van Bael, V. Goedseels, et al. "Electronic Identification and Physiological Monitoring: Data Collection Efficiency and Powering Requirements." Applied Engineering in Agriculture 10, no. 1 (1994): 105–7. http://dx.doi.org/10.13031/2013.25834.

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Leu, Fang−Yie, Chia−Yin Ko, Ilsun You, Kim-Kwang Raymond Choo, and Chi-Lun Ho. "A smartphone-based wearable sensors for monitoring real-time physiological data." Computers & Electrical Engineering 65 (January 2018): 376–92. http://dx.doi.org/10.1016/j.compeleceng.2017.06.031.

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Zhang, Zhen, and Fang Liu. "Design of Multi-Channel Physiological Signal Monitor Based on Wireless Data Transmission." Applied Mechanics and Materials 496-500 (January 2014): 1207–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1207.

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In order to monitor important parameters of patient vital signal continuously in real-time manner for a long time, a multi-channel physiological signal monitor based on wireless data transmission was designed. The device can acquire 8-channel high-resolution signal of electromyography or electrocardiograph and transmit these signal to remote monitoring station using real-time wireless communication. The monitoring station implements data displaying, storing, analyzing and processing. Simulation analysis shows that the design of the monitor has a broad outlook of clinical applications, making p
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Dong, Wen, and Chong Xin Liu. "Design of Intelligent Telemedicine Monitoring Data Transmission Network." Applied Mechanics and Materials 341-342 (July 2013): 773–76. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.773.

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The paper propose a design of the local data transmission between human body physiological parameters acquisition terminal and local intelligent monitoring terminal based on the wireless transmission technique of Zigbee and Bluetooth. In order to realize the remote transmission between Local medical monitoring terminal and remote intelligent monitoring terminal, the paper give network solutions for distal doctors to provide remote treatment. The program of monitoring terminal introduces the data processing mechanism Handler and class AsyncTask to ensure the reliability and accuracy of the data
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Clarke, Malcolm. "The need for an integrated approach to remote monitoring of physiological data and activity data." Journal of Telemedicine and Telecare 20, no. 3 (2014): 159–60. http://dx.doi.org/10.1177/1357633x14529245.

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Dissertations / Theses on the topic "Monitoring physiological data"

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Zarneh, A. T. "Instrumentation for on-line autonomic function testing." Thesis, University of Bradford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371492.

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Matsiras, Paul V. "PONI : an intelligent alarm system for respiratory and circulatory management in the operating rooms." Thesis, City University London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252926.

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Islam, Asif Moinul. "Case Based Reasoning method for analysing Physiological sensor data." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-27725.

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Remote healthcare is a demanding as well as emergent research area. The rise of healthcare costs in the developed countries have made the policy makers for trying to find an alternate model of healthcare rather than relying on traditional healthcare system. Although advancement in the sensor technology, forthcomingness of devices like smart phones and improvement in artificial intelligence technology have made the remote healthcare close to reality but still there are plenty of issues to be solved before it becomes a commonly used healthcare model. In this thesis, studies of two vital physiolo
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Islam, Asif Moinul. "Case Based Reasoning method for analysis of Physiological sensor data." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-27672.

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Remote healthcare is a demanding as well as emergent research area. The rise of healthcare costs in the developed countries have made the policy makers for trying to find an alternate model of healthcare rather than relying on traditional healthcare system. Although advancement in the sensor technology, forthcomingness of devices like smart phones and improvement in artificial intelligence technology have made the remote healthcare close to reality but still there are plenty of issues to be solved before it becomes a commonly used healthcare model. In this thesis, studies of two vital physiolo
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Shah, Syed Ahmar. "Vital sign monitoring and data fusion for paediatric triage." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:80ae66e3-849b-4df1-b064-f9eb7530200d.

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Accurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in both primary care (GP consultations) and secondary care (ED consultations). Serious illnesses such as meningitis and pneumonia account for 20% of deaths in childhood and require early recognition and treatment in order to maximize the chances of survival of affected children. Due to time constraints, poorly defined normal ranges, difficulty in achieving accurate readings and the difficulties faced by clinicians in interpreting combinations of vital signs, vit
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Zhang, Ying 1976. "Real-time analysis of physiological data and development of alarm algorithms for patient monitoring in the Intensive Care Unit." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/18026.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 86-90).<br>The lack of effective data integration and knowledge representation in patient monitoring limits its utility to clinicians. Intelligent alarm algorithms that use artificial intelligence techniques have the potential to reduce false alarm rates and to improve data integration and knowledge representation. Crucial to the development of such algorithms is a well-annotated data set. In previous studies, clinical events were eithe
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Patillo, Paul L., and Maureen J. O'Connor. "Reengineering human performance and fatigue research through use of physiological monitoring devices, web-based and mobile device data collection methods, and integrated data storage techniques." Thesis, Monterey, California. Naval Postgraduate School, 2003. http://hdl.handle.net/10945/6156.

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Approved for public release; distribution is unlimited<br>In the field of human research, particularly in operational environments, data collection techniques are difficult. Researchers often focus their efforts on the data analysis and overlook the shortcomings of their data collection and storage methodologies. In order to demonstrate effective data collection and storage methodology in a representative human research process, the process used by human fatigue and performance researchers at the Human Systems Integration Lab at Naval Postgraduate School (NPS) served as a Proof of Concept. Mos
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O'Connor, Maureen J. Patillo Paul J. "Reengineering human performance and fatigue research through use of physiological monitoring devices, web-based and mobile device data collection methods, and integrated data storage techniques /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FO'Connor.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, December 2003.<br>Thesis advisor(s): Nita L. Miller, Thomas J. Housel. Includes bibliographical references (p. 115-117). Also available online.
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Emeka-Nweze, Chika Cornelia. "ICU_POC: AN EMR-BASED POINT OF CARE SYSTEM DESIGN FOR THE INTENSIVE CARE UNIT." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1499255523449397.

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Puertas, Monica A. "Statistical and Prognostic Modeling of Clinical Outcomes with Complex Physiologic Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5106.

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Laboratory tests are a primary resource for diagnosing patient diseases. However, physicians often make decisions based on a single laboratory result and have a limited perspective of the role of commonly-measured parameters in enhancing the diagnostic process. By providing a dynamic patient profile, the diagnosis could be more accurate and timely, allowing physicians to anticipate changes in the recovery trajectory and intervene more effectively. The assessment and monitoring of the circulatory system is essential for patients in intensive care units (ICU). One component of this system is the
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Books on the topic "Monitoring physiological data"

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Green, Allan. The analysis of low frequency physiological monitoring signals: Application to capnograph data. University of Birmingham, 1998.

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Sharkey, Scott W. A guide to interpretation of hemodynamic data in the coronary care unit. Lippincott-Raven Publishers, 1997.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] congressional requesters. The Office, 2000.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] congressional requesters. The Office, 2000.

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Automation in anesthesia, a relief?: A systematic approach to computers in patient monitoring. Springer-Verlag, 1987.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] congressional requesters. The Office, 2000.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] Congressional requesters. The Office, 2000.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] congressional requesters. The Office, 2000.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : report to Congressional requesters. The Office, 2000.

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Office, General Accounting. Toxic chemicals: Long-term coordinated strategy needed to measure exposures in humans : [report to] Congressional requesters. The Office, 2000.

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Book chapters on the topic "Monitoring physiological data"

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Weil, André R., T. Cornelis Jansen, Harry N. Lafeber, and Willem P. F. Fetter. "Multichannel Recording and Analysis of Physiological Data Using a Personal Computer." In Continuous Transcutaneous Monitoring. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1927-6_55.

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Vaidya, Madhav, Nidhi Dahatkar, and Balika Kolhe. "Predictive Monitoring System Using K-NN, QDC Classifiers of Physiological Data." In Information and Communication Technology for Intelligent Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1747-7_18.

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Yang, Hui, Yun Chen, and Fabio Leonelli. "Characterization and Monitoring of Nonlinear Dynamics and Chaos in Complex Physiological Systems." In Healthcare Analytics: From Data to Knowledge to Healthcare Improvement. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118919408.ch3.

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Kacer, Jiri, Vaclav Krivanek, Ludek Cicmanec, et al. "Physiological Data Monitoring of Members of Air Forces During Training on Simulators." In IFMBE Proceedings. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9023-3_154.

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Dutta, Haimonti, Hillol Kargupta, and Anupam Joshi. "Orthogonal Decision Trees for Resource-Constrained Physiological Data Stream Monitoring Using Mobile Devices." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11602569_16.

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Rodrigues, Clarissa, William R. Fröhlich, Amanda G. Jabroski, Sandro J. Rigo, Andreia Rodrigues, and Elisa Kern de Castro. "Evaluating a New Approach to Data Fusion in Wearable Physiological Sensors for Stress Monitoring." In Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61380-8_37.

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Hercegfi, Károly, Olga Csillik, Éva Bodnár, Judit Sass, and Lajos Izsó. "Designers of Different Cognitive Styles Editing E-Learning Materials Studied by Monitoring Physiological and Other Data Simultaneously." In Engineering Psychology and Cognitive Ergonomics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02728-4_19.

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Peruzzini, Margherita, Fabio Grandi, Marcello Pellicciari, and Claudia Elisabetta Campanella. "User Experience Analysis Based on Physiological Data Monitoring and Mixed Prototyping to Support Human-Centre Product Design." In Advances in Ergonomics in Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94706-8_44.

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Hercegfi, Károly, Márton Pászti, Sarolta Tóvölgyi, and Lajos Izsó. "Usability Evaluation by Monitoring Physiological and Other Data Simultaneously with a Time-Resolution of Only a Few Seconds." In Human-Computer Interaction. New Trends. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02574-7_7.

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Grist, James T., Esben Søvsø Hansen, Frank G. Zöllner, and Christoffer Laustsen. "Sodium (23Na) MRI of the Kidney: Experimental Protocol." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_28.

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AbstractSodium handling is a key physiological hallmark of renal function. Alterations are generally considered a pathophysiologic event associated with kidney injury, with disturbances in the corticomedullary sodium gradient being indicative of a number of conditions. This experimental protocol review describes the individual steps needed to perform 23Na MRI; allowing accurate monitoring of the renal sodium distribution in a step-by-step experimental protocol for rodents.This chapter is based upon work from the PARENCHIMA COST Action, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.
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Conference papers on the topic "Monitoring physiological data"

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Wailoo, M. "Home monitoring of body temperature patterns in infancy." In IEE Colloquium on Data Logging of Physiological Signals. IEE, 1995. http://dx.doi.org/10.1049/ic:19951386.

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Shevchuk, Bohdan, Mykhaylo Geraimchuk, Orest Ivakhiv, and Yuriy Brayko. "Remote monitoring of the person physiological state." In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2017. http://dx.doi.org/10.1109/idaacs.2017.8095182.

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Shueng, Pei-Wei, Ming-Che Wu, Huai-Kuei Wu, et al. "Development of Physiological Data Monitoring System for Cancer Radiotherapy." In 2019 8th International Conference on Innovation, Communication and Engineering (ICICE). IEEE, 2019. http://dx.doi.org/10.1109/icice49024.2019.9117515.

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Pradhan, Gaurav N., Rita Chattopadhyay, and S. Panchanathan. "Processing body sensor data streams for continuous physiological monitoring." In the international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1743384.1743468.

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Qi, Huan, Z. Jane Wang, and Chunyan Miao. "Non-contact driver cardiac physiological monitoring using video data." In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015. http://dx.doi.org/10.1109/chinasip.2015.7230436.

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Awolusi, Ibukun, Eric Marks, and Matthew Hallowell. "Physiological Data Collection and Monitoring of Construction Equipment Operators." In Construction Research Congress 2016. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479827.293.

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Georgieva-Tsaneva, Galya. "Body Sensors System for Physiological Data Long-term Monitoring." In CompSysTech '20: International Conference on Computer Systems and Technologies '20. ACM, 2020. http://dx.doi.org/10.1145/3407982.3408009.

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Yang, Sungwon, Jihyoung Kim, and Mario Gerla. "Clinical quality guaranteed physiological data compression in mobile health monitoring." In the 2nd ACM international workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2248341.2248351.

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Liu, Qiang, Datong Chen, Mingui Sun, and Robert J. Sclabassi. "Specialized Video and Physiological Data Coding System for Remote Monitoring." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262605.

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Chang, Yu-Hsiang, Ai-Lun Yang, Yung-Sheng Lin, and Yue-Shan Chang. "Monitoring and estimating inhalation of particular matter using personal physiological data." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8122716.

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Reports on the topic "Monitoring physiological data"

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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ e
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Buller, Mark J., Rob M. Siegel, Gary P. Vaillette, Debra Meyers, and William T. Matthew. Automated Data Management for Warfighter Physiologic Status Monitoring. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada398541.

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