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An AED-UAV Healthcare Information Robot Integration System Based on Edge Computing Airport Station

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Published:30 October 2022Publication History

ABSTRACT

The Unmanned Aerial Vehicle (UAV) has gradually become a type of tool in people's lives, especially with the development of 5G and edge computing, more medical facilities can be leveraged via UAV delivery. Rapid delivery of Automated External Defibrillator (AED) is very important for the rescue of heart attack patients, but this field faces some drawbacks from reliability and information management. In this paper, we discuss making AED into UAV to reduce weight, and designing some edge computing stations to realize remote distributed control. The first design process is by estimating take-off weight and flight time. The electrical capacity is then estimated to treat the patient through electrical therapy that multiple charge power is required. The second design is an information system which is used to Message Queuing Telemetry Transport (MQTT) with Modbus protocol to control the flight path and attitude of UAV. Lastly, we discuss the feasibility of the integration of these information electromechanical systems, and plan to implement it in the future work.

References

  1. Biczyski, M. 2020. Multirotor Sizing Methodology with Flight Time Estimation. Journal of Advanced Transportation. 2020, (2020). DOI:https://doi.org/10.1155/2020/9689604.Google ScholarGoogle ScholarCross RefCross Ref
  2. Boon, M.A. 2017. Comparison of a fixed-wing and multi-rotor UAV for environmental mapping applications: A case study. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42, 2W6 (2017), 47–54. DOI:https://doi.org/10.5194/isprs-archives-XLII-2-W6-47-2017.Google ScholarGoogle Scholar
  3. Chung, S.P. 2016. The 2015 Resuscitation Council of Asia (RCA) guidelines on adult basic life support for lay rescuers. Resuscitation. 105, (2016), 145–148. DOI:https://doi.org/10.1016/j.resuscitation.2016.05.025.Google ScholarGoogle Scholar
  4. Ferretti, J. 2017. Open-source automated external defibrillator. HardwareX. 2, (2017), 61–70. DOI:https://doi.org/10.1016/j.ohx.2017.09.001.Google ScholarGoogle Scholar
  5. Gong, A. 2018. Performance testing and modeling of a brushless dc motor, electronic speed controller and propeller for a small uav. 2018 Joint Propulsion Conference. July (2018). DOI:https://doi.org/10.2514/6.2018-4584.Google ScholarGoogle ScholarCross RefCross Ref
  6. Guo-Gang. and Yan, S.-J.L.C.-Z. and Z.H. and C.S. and L.X.-R. and T. 2020. Consensus statement on layout and delivery of automatic external defibrillator in China. Journal of Acute Disease. 9, 5 (2020), 183. DOI:https://doi.org/10.4103/2221-6189.291281.Google ScholarGoogle ScholarCross RefCross Ref
  7. Kane, E.M. 2019. Use of Systems Engineering to Design a Hospital Command Center. Joint Commission Journal on Quality and Patient Safety. 45, 5 (2019), 370–379. DOI:https://doi.org/10.1016/j.jcjq.2018.11.006.Google ScholarGoogle ScholarCross RefCross Ref
  8. Kiyohara, K. 2016. Public-access AED pad application and outcomes for out-of-hospital cardiac arrests in Osaka, Japan. Resuscitation. 106, (2016), 70–75. DOI:https://doi.org/10.1016/j.resuscitation.2016.06.025.Google ScholarGoogle ScholarCross RefCross Ref
  9. KROLL, M.W. 1994. A Minimal Model of the Single Capacitor Biphasic Defibrillation Waveform. Pacing and Clinical Electrophysiology. 17, 11 (1994), 1782–1792. DOI:https://doi.org/10.1111/j.1540-8159.1994.tb03746.x.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kumar, M. 2020. Healthcare Solution based on Machine Learning Applications in IOT and Edge Computing Edge Computing View project Cloud Computing System Models View project. 119, 16 (2020), 1473–1484.Google ScholarGoogle Scholar
  11. Lee, M.T. 2022. UAV Swarm Real-Time Rerouting by Edge Computing D* Lite Algorithm. Applied Sciences (Switzerland). 12, 3 (Jan. 2022), 1056. DOI:https://doi.org/10.3390/app12031056.Google ScholarGoogle Scholar
  12. Li, Y. 2015. Abstract 16857: Design and Implementation of a Prehospital Electrocardiogram Remote Transmission and Data Recording App for Acute Myocardial Infarction Based on WeChat in China. Circulation. 132, suppl_3 (Nov. 2015). DOI:https://doi.org/10.1161/CIRC.132.SUPPL_3.16857.Google ScholarGoogle Scholar
  13. Li, Z. 2018. 5G URLLC: Design challenges and system concepts. Proceedings of the International Symposium on Wireless Communication Systems. 2018-Augus, August (2018). DOI:https://doi.org/10.1109/ISWCS.2018.8491078.Google ScholarGoogle ScholarCross RefCross Ref
  14. Lim, J.H. 2017. An emergency call system for patients in locked-in state using an SSVEP-based brain switch. Psychophysiology. 54, 11 (2017), 1632–1643. DOI:https://doi.org/10.1111/psyp.12916.Google ScholarGoogle ScholarCross RefCross Ref
  15. Liu, B. 2019. Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT. Advanced Engineering Informatics. 42, April (2019), 100984. DOI:https://doi.org/10.1016/j.aei.2019.100984.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Liu, Y. 2020. Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things. IEEE Internet of Things Journal. 7, 8 (2020), 6722–6747. DOI:https://doi.org/10.1109/JIOT.2020.3004500.Google ScholarGoogle ScholarCross RefCross Ref
  17. Patel, M. and Patel, R. 2021. Improved identity based encryption system (Iibes): A mechanism for eliminating the key-escrow problem. Emerging Science Journal. 5, 1 (2021), 77–84. DOI:https://doi.org/10.28991/esj-2021-01259.Google ScholarGoogle ScholarCross RefCross Ref
  18. Qu, Y. 2021. Service Provisioning for UAV-Enabled Mobile Edge Computing. IEEE Journal on Selected Areas in Communications. 39, 11 (2021), 3287–3305. DOI:https://doi.org/10.1109/JSAC.2021.3088660.Google ScholarGoogle ScholarCross RefCross Ref
  19. Sacoto Cabrera, E.J. 2021. Industrial Communication Based on MQTT and Modbus Communication Applied in a Meteorological Network. Advances in Intelligent Systems and Computing. 1302, (2021), 29–41. DOI:https://doi.org/10.1007/978-3-030-63665-4_3.Google ScholarGoogle Scholar
  20. Salih, A.L. 2010. Flight PID controller design for a UAV quadrotor. Scientific Research and Essays. 5, 23 (2010), 3660–3667.Google ScholarGoogle Scholar
  21. Sarkisian, L. 2020. Global positioning system alerted volunteer first responders arrive before emergency medical services in more than four out of five emergency calls. Resuscitation. 152, (2020), 170–176. DOI:https://doi.org/10.1016/j.resuscitation.2019.12.010.Google ScholarGoogle Scholar
  22. Seoane, V. 2021. Performance evaluation of CoAP and MQTT with security support for IoT environments. Computer Networks. 197, April (2021), 108338. DOI:https://doi.org/10.1016/j.comnet.2021.108338.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Soar, J. 2021. European resuscitation council guidelines 2021: adult advanced life support. Elsevier. (2021). DOI:https://doi.org/10.1016/j.resuscitation.2021.02.010.Google ScholarGoogle Scholar
  24. Su, W. 2021. ICICOS: Industrial Cyber Intelligent Control Operating System for Cloud and Edge Computing. 2021 IEEE International Conference on Smart Internet of Things (SmartIoT) (Oct. 2021), 311–316.Google ScholarGoogle Scholar
  25. Su, W. 2019. Three-real-time architecture of industrial automation based on edge computing. Proceedings - 2019 IEEE International Conference on Smart Internet of Things, SmartIoT 2019 (2019), 372–377.Google ScholarGoogle Scholar
  26. Teixeira, N. 2021. Data Access Mechanism to Allow Multiple Level Permissions in Energy Management Solutions Supported by IoT devices. 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings (2021), 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  27. Tomaszewski, L. 2020. On 5G support of cross-border UAV operations. 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings. (2020), 1–8. DOI:https://doi.org/10.1109/ICCWorkshops49005.2020.9145262.Google ScholarGoogle ScholarCross RefCross Ref
  28. V4008 SUNNYSKY More stable power system: http://www.rcsunnysky.com/v-multi-rotorefficiencytype/1082.html. Accessed: 2022-04-23.Google ScholarGoogle Scholar
  29. Viereck, S. 2017. Effect of bystander CPR initiation prior to the emergency call on ROSC and 30 day survival—An evaluation of 548 emergency calls. Resuscitation. 111, (2017), 55–61. DOI:https://doi.org/10.1016/j.resuscitation.2016.11.020.Google ScholarGoogle Scholar
  30. Weinlich, M. 2018. Significant acceleration of emergency response using smartphone geolocation data and a worldwide emergency call support system. PLoS ONE. 13, 5 (2018), 1–10. DOI:https://doi.org/10.1371/journal.pone.0196336.Google ScholarGoogle ScholarCross RefCross Ref
  31. Wilson, S.O. 2018. Control and Monitoring of Electrical Variables of a Level Process using Modbus RTU-TCP/IP Industrial Communication. Indian Journal of Science and Technology. 11, 32 (2018), 1–12. DOI:https://doi.org/10.17485/ijst/2018/v11i32/131113.Google ScholarGoogle ScholarCross RefCross Ref
  32. Woollard, M. 2004. Skill acquisition and retention in automated external defibrillator (AED) use and CPR by lay responders: A prospective study. Resuscitation. 60, 1 (2004), 17–28. DOI:https://doi.org/10.1016/j.resuscitation.2003.09.006.Google ScholarGoogle ScholarCross RefCross Ref
  33. Yang, B. 2016. Lithium difluorophosphate as an additive to improve the low temperature performance of LiNi0.5Co0.2Mn0.3O2/graphite cells. Electrochimica Acta. 221, (Dec. 2016), 107–114. DOI:https://doi.org/10.1016/j.electacta.2016.10.037.Google ScholarGoogle ScholarCross RefCross Ref
  34. Zègre-Hemsey, J.K. 2018. Delivery of Automated External Defibrillators (AED) by Drones: Implications for Emergency Cardiac Care. Current Cardiovascular Risk Reports. 12, 11 (2018), 3–7. DOI:https://doi.org/10.1007/s12170-018-0589-2.Google ScholarGoogle ScholarCross RefCross Ref
  35. Zhang, S. and Wang, Y. 2016. The simulation of BLDC motor speed control based-optimized fuzzy PID algorithm. 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016. (2016), 287–292. DOI:https://doi.org/10.1109/ICMA.2016.7558576.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    ICRSA 2022: 2022 the 5th International Conference on Robot Systems and Applications (ICRSA)
    June 2022
    89 pages
    ISBN:9781450396486
    DOI:10.1145/3556267

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    Publication History

    • Published: 30 October 2022

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