Solution pattern for machine-to-cloud integration in medical robotics

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Industrial Revolution 4.0 is bringing a paradigm shift into the industrial world and also indirectly revolutionising the healthcare domain. The Internet of Things (IoT) brings a revolution in the healthcare industry and medical education. One of the vital developments involves integration of medical devices and robots with cloud through the internet. This paper thus describes a general solution guideline for developing the IoT compliant medical robots. For that, an upper limb part-task trainer for upper limb spasticity evaluation training for rehabilitation named BITA is used to demonstrate how cloud integration could be achieved at its system-level design.




  • Keywords

    Machine to Cloud Integration; Cloud Technology; Medical Robotics; Medical Education; Smart Healthcare.

  • References

      [1] C. Coles, “11 Advantages of Cloud Computing and How Your Business Can Benefit From Them,” 2016. [Online]. Available: [Accessed: 11-Nov-2017].

      [2] L. Wieclaw, V. Pasichnyk, N. Kunanets, O. Duda, O. Matsiuk, and P. Falat, “Cloud computing technologies in ‘smart city’ projects,” in 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017, pp. 339–342.

      [3] C. O. Rolim, F. L. Koch, C. B. Westphall, J. Werner, A. Fracalossi, and G. S. Salvador, “A Cloud Computing Solution for Patient’s Data Collection in Health Care Institutions,” in 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine, 2010, no. ii, pp. 95–99.

      [4] Amit Kumawat, “Cloud Service Models (IaaS, SaaS, PaaS) + How Microsoft Office 365, Azure Fit In,” 2013. .

      [5] M. Ferkoun, “Top 7 most common uses of cloud computing,” 2014. [Online]. Available: [Accessed: 09-Nov-2017].

      [6] M. Rouse, “What is cloud analytics?” [Online]. Available: [Accessed: 09-Nov-2017].

      [7] M. Kralj, “How cloud serves as the foundation of AI,” 2017. [Online]. Available: [Accessed: 09-Nov-2017].

      [8] N. A. Cz, T. Komeda, and C. Y. Low, “Design of Upper Limb Patient Simulator,” Procedia Eng., vol. 41, no. Iris, pp. 1374–1378, 2012.

      [9] F. Idris, N. A. C. Zakaria, C. Y. Low, F. A. Hanapiah, and N. A. Othman, “System Integration of an Upper Limb Disorder Part-Task Trainer with PC-based Control,” Procedia Comput. Sci., vol. 105, no. December 2016, pp. 328–332, 2017.

      [10] N. A. Othman, F. Idris, N. A. C. Zakaria, F. A. Hanapiah, and C. Y. Low, “Supporting clinical evaluation of upper limb spasticity with quantitative data measurement in accordance to the Modified Ashworth Scale,” in 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016, pp. 731–736.

      [11] N. A. Othman, N. A. Che Zakaria, C. Y. Low, F. A. Hanapiah, T. Komeda, and K. Inoue, “Towards a Clinically Compliant Upper Limb Part-Task Trainer in Simulated Learning Program,” J. Teknol., vol. 76, no. 4, pp. 71–76, Sep. 2015.

      [12] N. A. Cz, T. Komeda, C. Y. Low, and K. Inoue, “Emulation of muscle tone of upper limb spasticity and rigidity,” in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013, pp. 1590–1593.

      [13] J. Gausemeier, C. Y. Low, D. Steffen, and S. Deyter, “Specifying the Principle Solution in Mechatronic Development Enterprises,” in 2008 second Annual IEEE Systems Conference, 2008, pp. 1–7.




Article ID: 20873
DOI: 10.14419/ijet.v7i4.20873

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.