PROTOTYPE OF A CYBER-PHYSICAL SYSTEM FOR MONITORING THE PHYSICAL CONDITION OF THE AIRCRAFT OPERATOR
DOI:
https://doi.org/10.26906/SUNZ.2022.4.057Keywords:
biomedical complex, WEB-server, IoT devices, cybernetic system, Arduino, ESP, hardware, softwareAbstract
Telephones, personal computers, cars, trains, planes – all these devices have a huge number of sensors that allow to determine the current state of almost every system of the device. But they do not take into account the psychoemotional and physical state of operators while driving such complex device. According to statistics, more than half of air crashes occur due to the human factor. To reduce the number of air crashes associated with the deterioration of the aircraft operator, it is proposed to use a mobile biomedical complex with software and hardware parts. The developed complex should take the main biological indicators of the operator in real time, store and process them, this data can be used to give advice to the operator to improve his condition. The hardware includes the following units for processing data from sensors: cardiograph; four myographs; pulse oximeter; temperature and humidity; determination of skin resistance. Data from the data processor units are collected by the microcontroller-processor, which can further convert the data into adequate physical quantities. The microcontroller-processor exchanges data with the microcontroller-server, which is designed to buffer and output data to the user’s device or to cloud WEB-services. The software part includes lower-level programs for collecting measurement data, processing them, forming them into packets, exchanging packets between microcontrollers and outputting data to WEB services, as well as Backend WEB pages of the user interface. The top-level programming includes the development of WEB-pages where the current information about the state of the examined user is displayed. Currently, a model of a biomedical complex based on the Arduino UNO and NodeMCU platforms has been created, which can measure skin resistance, humidity and respiratory temperature, as well as transmit them to clients located in the local network. In the future, it is planned to: develop a system for storing data and sending them to connected users; improve the user interface and implement the functionality of quick reconfiguration of the monitoring functions of the complex; create a data processing system based on information and analytical decision support tools to generate individual recommendations for improving the physical condition of the operator.Downloads
References
Rodríguez-Jorge, R., De León-Damas, I., Bila, J., & Škvor, J. (2021). Internet of things-assisted architecture for QRS complex detection in real time. Internet of Things, 14, 100395. doi: https://doi.org/10.1016/j.iot.2021.100395
De Giovanni, E., Forooghifar, F., Surrel, G., Teijeiro, T., Peon, M., Aminifar, A., & Atienza Alonso, D. (2022). Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era. In Emerging Computing: From Devices to Systems (pp. 407-433). Springer, Singapore. doi: https://doi.org/10.1007/978-981-16-7487-7_13
Mora, H., Gil, D., Munoz Terol, R., Azorín, J., & Szymanski, J. (2017). An IoT-based computational framework for healthcare monitoring in mobile environments. Sensors, 17(10), 2302. doi: https://doi.org/10.3390/s17102302
ESP32/ESP8266 Plot Sensor Readings in Real Time Charts | Random Nerd Tutorials. (n.d.). Random Nerd Tutorials. https://randomnerdtutorials.com/esp32-esp8266-plot-chart-web-server/
Olkhova, Y., Guchenko, M. (2015). Creation of a Local Model of a Neuron-Controlled Process Network. Electromechanical Systems, Modeling and Optimization Methods (p. 318). KrNU, Ukraine. URL: http://www.kdu.edu.ua/statti/Tezi/Tezi_EES_%20pdf/318.PDF
Guchenko, M., Kostenko P., Slavko O., Sokhin N. (2015). A Formal Model of Information Technology for Improving the Quality of Service of Data Flows Based on a Local Model of The Controlled Process. Problems of informatization and management, 3(51), KrNU, Ukraine. URL: https://jrnl.nau.edu.ua/index.php/PIU/article/view/10305/13567
Zagirnyak, M., Perekrest, A., Ogar, V., Chebotarova, Y., & Mur, O. (2021). Segmentation of heat energy consumers based on data on daily power consumption. Natsional’nyi Hirnychyi Universytet. Naukovyi Visnyk, (2), 89-96. URL: http://www.nvngu.in.ua/jdownloads/pdf/2021/2/02_2021_Zagirnyak.pdf
Perekrest, A., Chenchevoi, V., Chencheva, O., Kovalenko, A., Kushch-Zhyrko, M., Kalizhanova, A., & Amirgaliyev, Y. (2022). Prediction Model of Public Houses’heating Systems: a Comparison of Support Vector Machine Method and Random Forest Method. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 12(3), 34-39. URL: https://ph.pollub.pl/index.php/iapgos/article/view/3032/2723
