Acquisition and Processing of Data in Cps For Remote Monitoring of the Human Functional State

Simple item page
dc.citation.epage20
dc.citation.issue1
dc.citation.spage14
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorHupalo, Petro
dc.contributor.authorMelnyk, Anatoliy
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2022-05-24T08:53:06Z
dc.date.available2022-05-24T08:53:06Z
dc.date.created2021-03-01
dc.date.issued2021-03-01
dc.description.abstractData acquisition and processing in cyberphysical system for remote monitoring of the human functional state have been considered in the paper. The data processing steps, strategies for multi-step forecasting evaluation metrics and machine learning algorithms to be implemented have been analysed and described. What is important, this way it will be possible to track the condition of the sick and response to the health changes in advance.
dc.format.extent14-20
dc.format.pages7
dc.identifier.citationHupalo P. Acquisition and Processing of Data in Cps For Remote Monitoring of the Human Functional State / Petro Hupalo, Anatoliy Melnyk // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 14–20.
dc.identifier.citationenHupalo P. Acquisition and Processing of Data in Cps For Remote Monitoring of the Human Functional State / Petro Hupalo, Anatoliy Melnyk // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 14–20.
dc.identifier.doihttps://doi.org/10.23939/acps2021.01.014
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56846
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 1 (6), 2021
dc.relation.references[1] Machine Learning in Wearable Biomedical Systems, August 2020, url: https://www.intechopen.com/books/sportsscience-and-human-health-different-approaches/machinelearning-in-wearable-biomedical-systems
dc.relation.references[2] Anatoliy Melnyk, Yurii Morozov, Bohdan Havano, Petro Hupalo. HealthSupervisor: Mobile Application for Round-the-Clock Remote Monitoring of the Human Functional State (keynote). Proceedings of the 2nd International Workshop on Intelligent Information Technologies & Systems of Information Security with CEUR-WS. Khmelnytskyi, Vol. 2853, Ukraine, March 24–26, 2021, pp. 24–37. – http://ceur-ws.org/Vol-2853/
dc.relation.references[3] Sang M. Lee, DonHee Lee, Healthcare wearable devices: an analysis of key factors for continuous use intention, October 2020, url: https://link.springer.com/article/10.1007/s11628-020-00428-3
dc.relation.references[4] Aras R. Dargazany, Paolo Stegagno, and Kunal Mankodiya, WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics – Concept, Literature, and Future, Mobile Information Systems, November 2018
dc.relation.references[5] Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer WristWorn Wearables, Journal of Medical Internet Research, March 2018, url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887043/
dc.relation.references[6] Anatoliy Melnyk, Yuriy Morozov, Bohdan Havanio, Petro Hupalo. Investigation of Wireless Pulse Oximeters for Smartphone-based Remote Monitoring of Lung Health. Advances in Cyber-Physical Systems. 2020; Vol. 5, Nr 2: pp. 70–76. https://doi.org/10.23939/acps2020.02.070
dc.relation.references[7] Anatoliy Melnyk, Yuriy Morozov, Bohdan Havanio, Petro Hupalo. Investigation of Wireless Pulse Oximeters for Smartphone-based Remote Monitoring of Lung Health. Advances in Cyber-Physical Systems. 2020; Vol. 5, Nr 2: pp. 70–76. https://doi.org/10.23939/acps2020.02.070
dc.relation.references[8] Ben Taieb, S., Bontempi, G., Atiya, A., Sorjamaa, A.: A review and comparison ofstrategies for multi-step ahead time series forecasting based on the NN5 forecastingcompetition. ArXiv e-prints, August 2011
dc.relation.references[9] Jason Brownlee. Introducing to time series forecasting with Python, 2017
dc.relation.references[10] Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis and future prospects. J Big Data 6, 54 (2019). https://doi.org/10.1186/s40537-019-0217-0.
dc.relation.references[11] M Supriya, AJ Deepa, Machine learning approach on healthcare big data: a review, Big Data and Information Analytics, 2020, Vol. 5, Issue 1: 58–75. October 2020
dc.relation.references[12] Aras R. Dargazany, Paolo Stegagno, and Kunal Mankodiya, WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics – Concept, Literature, and Future, Mobile Information Systems, November 2018
dc.relation.references[13] Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le Borgne, Machine Learning Strategies for Time Series Forecasting. Lecture Notes in Business Information Processing, January 2013, p. 138, url: https://www.researchgate.net/publication/236941795_Machine_Learning_Strategies_for_Time_Series_Forecasting
dc.relation.references[14] Jason Brownlee, Deep Learning for time series forecasting, August 2018
dc.relation.referencesen[1] Machine Learning in Wearable Biomedical Systems, August 2020, url: https://www.intechopen.com/books/sportsscience-and-human-health-different-approaches/machinelearning-in-wearable-biomedical-systems
dc.relation.referencesen[2] Anatoliy Melnyk, Yurii Morozov, Bohdan Havano, Petro Hupalo. HealthSupervisor: Mobile Application for Round-the-Clock Remote Monitoring of the Human Functional State (keynote). Proceedings of the 2nd International Workshop on Intelligent Information Technologies & Systems of Information Security with CEUR-WS. Khmelnytskyi, Vol. 2853, Ukraine, March 24–26, 2021, pp. 24–37, http://ceur-ws.org/Vol-2853/
dc.relation.referencesen[3] Sang M. Lee, DonHee Lee, Healthcare wearable devices: an analysis of key factors for continuous use intention, October 2020, url: https://link.springer.com/article/10.1007/s11628-020-00428-3
dc.relation.referencesen[4] Aras R. Dargazany, Paolo Stegagno, and Kunal Mankodiya, WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics – Concept, Literature, and Future, Mobile Information Systems, November 2018
dc.relation.referencesen[5] Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer WristWorn Wearables, Journal of Medical Internet Research, March 2018, url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887043/
dc.relation.referencesen[6] Anatoliy Melnyk, Yuriy Morozov, Bohdan Havanio, Petro Hupalo. Investigation of Wireless Pulse Oximeters for Smartphone-based Remote Monitoring of Lung Health. Advances in Cyber-Physical Systems. 2020; Vol. 5, Nr 2: pp. 70–76. https://doi.org/10.23939/acps2020.02.070
dc.relation.referencesen[7] Anatoliy Melnyk, Yuriy Morozov, Bohdan Havanio, Petro Hupalo. Investigation of Wireless Pulse Oximeters for Smartphone-based Remote Monitoring of Lung Health. Advances in Cyber-Physical Systems. 2020; Vol. 5, Nr 2: pp. 70–76. https://doi.org/10.23939/acps2020.02.070
dc.relation.referencesen[8] Ben Taieb, S., Bontempi, G., Atiya, A., Sorjamaa, A., A review and comparison ofstrategies for multi-step ahead time series forecasting based on the NN5 forecastingcompetition. ArXiv e-prints, August 2011
dc.relation.referencesen[9] Jason Brownlee. Introducing to time series forecasting with Python, 2017
dc.relation.referencesen[10] Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis and future prospects. J Big Data 6, 54 (2019). https://doi.org/10.1186/s40537-019-0217-0.
dc.relation.referencesen[11] M Supriya, AJ Deepa, Machine learning approach on healthcare big data: a review, Big Data and Information Analytics, 2020, Vol. 5, Issue 1: 58–75. October 2020
dc.relation.referencesen[12] Aras R. Dargazany, Paolo Stegagno, and Kunal Mankodiya, WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics – Concept, Literature, and Future, Mobile Information Systems, November 2018
dc.relation.referencesen[13] Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le Borgne, Machine Learning Strategies for Time Series Forecasting. Lecture Notes in Business Information Processing, January 2013, p. 138, url: https://www.researchgate.net/publication/236941795_Machine_Learning_Strategies_for_Time_Series_Forecasting
dc.relation.referencesen[14] Jason Brownlee, Deep Learning for time series forecasting, August 2018
dc.relation.urihttps://www.intechopen.com/books/sportsscience-and-human-health-different-approaches/machinelearning-in-wearable-biomedical-systems
dc.relation.urihttp://ceur-ws.org/Vol-2853/
dc.relation.urihttps://link.springer.com/article/10.1007/s11628-020-00428-3
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887043/
dc.relation.urihttps://doi.org/10.23939/acps2020.02.070
dc.relation.urihttps://doi.org/10.1186/s40537-019-0217-0
dc.relation.urihttps://www.researchgate.net/publication/236941795_Machine_Learning_Strategies_for_Time_Series_Forecasting
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.rights.holder© Hupalo P., Melnyk A., 2021
dc.subjectbiometric data
dc.subjectdata collection
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectremote monitoring of human state
dc.titleAcquisition and Processing of Data in Cps For Remote Monitoring of the Human Functional State
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2021. – Vol. 6, No. 1