Acquisition and Processing of Data in CPS for Remote Monitoring of the Human functional State

2021;
: pp. 14 - 20
1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, The John Paul II Catholic University of Lublin

Data acquisition and processing in cyber-physical 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.

  1. Machine Learning in Wearable Biomedical Systems, August 2020, url: https://www.intechopen.com/books/sports-science- and-human-health-different-approaches/machine-learning-in- wearable-biomedical-systems
  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/
  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
  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
    https://doi.org/10.1155/2018/8125126
  5. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist- Worn Wearables, Journal of Medical Internet Research, March 2018, url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887043/
  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; Volume 5, Number 2: pp. 70 — 76. https://doi.org/10.23939/acps2020.02.070
    https://doi.org/10.23939/acps2020.02.070
  7. 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
    https://doi.org/10.1016/j.eswa.2012.01.039
  8. Jason Brownlee. Introducing to time series forecasting with Python, 2017
  9. 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.
    https://doi.org/10.1186/s40537-019-0217-0
  10. M Supriya, AJ Deepa, Machine learning approach on healthcare big data: a review, Big Data and Information Analytics, 2020, Volume 5, Issue 1: 58-75. October 2020
    https://doi.org/10.3934/bdia.2020005
  11. 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
    https://doi.org/10.1155/2018/8125126
  12. Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le Borgne, Machine Learning Strategies for Time Series Forecasting. Lecture Notes in Business Information Processing, January 2013, pp.138, url: https://www.researchgate.net/publication/236941795_Machi ne_Learning_Strategies_for_Time_Series_Forecasting
    https://doi.org/10.1007/978-3-642-36318-4_3
  13. Jason Brownlee, Deep Learning for time series forecasting, August 2018