Conceptual model for informing user with innovative smart wearable device in industry 4.0

  • Marko PerišaEmail author
  • Tibor Mijo Kuljanić
  • Ivan Cvitić
  • Peter Kolarovszki


The everyday needs of people with disabilities represent a challenge in designing new and innovative services. The capabilities provided by modern communication technologies designed by assistive technology models can help people with disabilities perform their work tasks. The user environment has to be adapted to the guidelines of the universal design and services should be available regardless of user requirements. This paper proposes a conceptual model for informing the people with disabilities regardless of the disability degree using a smart wearable device (smart wristband). All relevant information is integrated through the warehouse management system to efficiently perform all the appropriate processes. Activity of informing users is viewed from multiple scenarios, the work environment and work tasks. For the purpose of real-time informing, industrial internet of things environment and appropriate sensors were used. The presented model aims at raising the quality of life for the people with disabilities in the work environment and rising business efficiency based on industry 4.0 concept.


Assistive technology Internet of things Smart environment Wireless sensor network 



  1. 1.
    Strange, R., & Zucchella, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25(3), 174–184. Scholar
  2. 2.
    Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in industry 4.0: A review of the concept and of energy management approached in production based on the internet of things paradigm. In 2014 IEEE international conference on industrial engineering and engineering management (pp. 697–701). IEEE.
  3. 3.
    Juntao, L. (2016). Research on internet of things technology application status in the warehouse operation. International Journal of Science, Technology and Society, 4(4), 63. Scholar
  4. 4.
    Madleňák, R., Madleňáková, L., & Kolarovszká, Z. (2016). System of management and traceability of logistic items through new technologies. Procedia-Social and Behavioral Sciences, 230(September), 128–135. Scholar
  5. 5.
    Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 40, 536–541. Scholar
  6. 6.
    Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3–8. Scholar
  7. 7.
    Staudt, F. H., Alpan, G., Di Mascolo, M., & Rodriguez, C. M. T. (2015). Warehouse performance measurement: A literature review. International Journal of Production Research, 53(18), 5524–5544. Scholar
  8. 8.
    Richards, G. (2018). In G. Richards (Ed.), Warehouse management: A complete guide to improving efficiency and minimizing costs in the modern warehouse (3rd ed.). London: Kogan Page.Google Scholar
  9. 9.
    Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability (Switzerland), 10(1), 1–24. Scholar
  10. 10.
    Poon, T. C., Choy, K. L., Chow, H. K. H., Lau, H. C. W., Chan, F. T. S., & Ho, K. C. (2009). A RFID case-based logistics resource management system for managing order-picking operations in warehouses. Expert Systems with Applications, 36(4), 8277–8301. Scholar
  11. 11.
    Quader, S., & Castillo-Villar, K. K. (2018). Design of an enhanced multi-aisle order-picking system considering storage assignments and routing heuristics. Robotics and Computer-Integrated Manufacturing, 50, 13–29. Scholar
  12. 12.
    Choy, K. L., Ho, G. T. S., & Lee, C. K. H. (2017). A RFID-based storage assignment system for enhancing the efficiency of order picking. Journal of Intelligent Manufacturing, 28(1), 111–129. Scholar
  13. 13.
    Feng, L. (2016). Intelligent logistics and distribution system based on internet of things. In 2016 IEEE advanced information management, communicates, electronic and automation control conference (IMCEC) (pp. 228–231). IEEE.
  14. 14.
    Thanapal, P., Prabhu, J., & Jakhar, M. (2017). A survey on barcode RFID and NFC. IOP Conference Series: Materials Science and Engineering. Scholar
  15. 15.
    Kose, U., & Vasant, P. (2018). Better campus life for visually impaired University students: Intelligent social walking system with beacon and assistive technologies. Wireless Networks. Scholar
  16. 16.
    Onasanya, A., Lakkis, S., & Elshakankiri, M. (2019). Implementing IoT/WSN based smart Saskatchewan healthcare system. Wireless Networks. Scholar
  17. 17.
    Periša, M., Marković, G., Kolarovszki, P., & Madleňák, R. (2019). Proposal of a conceptual architecture system for informing the user in the IoT environment. PROMET-Traffic & Transportation, 31(1), 37–47. Scholar
  18. 18.
    Peraković, D., Periša, M., & Cvitić, I. (2018). Analysis of the possible application of assistive technology in the concept of industry 4.0. In PosTel 2018XXXVI Simpozijum o novim tehnologijama u poštanskom i telekomunikacionom saobraćaju (pp. 175–184).Google Scholar
  19. 19.
    Morash-Macneil, V., Johnson, F., & Ryan, J. B. (2018). A systematic review of assistive technology for individuals with intellectual disability in the workplace. Journal of Special Education Technology, 33(1), 15–26. Scholar
  20. 20.
    Jarl, G., & Lundqvist, L.-O. (2018). An alternative perspective on assistive technology: The person–environment–tool (PET) model. Assistive Technology, 00(00), 1–7. Scholar
  21. 21.
    Wahidin, H., Waycott, J., & Baker, S. (2018). The challenges in adopting assistive technologies in the workplace for people with visual impairments. In Proceedings of the 30th Australian conference on computerhuman interactionOzCHI’18 (pp. 432–442). New York, NY: ACM Press.
  22. 22.
    Wang, I.-T., Lee, S.-J., Bezyak, J., Tsai, M.-W., Luo, H.-J., Wang, J.-R., et al. (2018). Factors associated with recommendations for assistive technology devices for persons with mobility limitations using workplace accommodation services. Rehabilitation Counseling Bulletin, 61(4), 228–235. Scholar
  23. 23.
    Tran, B. (2019). Assistive technology and human capital for workforce diversity. In Advanced methodologies and technologies in artificial intelligence, computer simulation, and humancomputer interaction (pp. 225–236).
  24. 24.
    Khalifa, G., Sharif, Z., Sultan, M., & Di Rezze, B. (2019). Workplace accommodations for adults with autism spectrum disorder: A scoping review. Disability and Rehabilitation, 8, 1–16. Scholar
  25. 25.
    Chunli, L., & Donghui, L. (2012). Application and development of RFID technique. In 2012 2nd International conference on consumer electronics, communications and networks (CECNet) (pp. 900–903). IEEE.
  26. 26.
    Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2017). Design and application of internet of things-based warehouse management system for smart logistics. International Journal of Production Research. Scholar
  27. 27.
    Ding, B., Chen, L., Chen, D., & Yuan, H. (2008). Application of RTLS in warehouse management based on RFID and Wi-Fi. In 2008 4th International conference on wireless communications, networking and mobile computing (pp. 1–5). IEEE.
  28. 28.
    Peraković, D., Periša, M., & Sente, R. E. (2018). Information and communication technologies within industry 4.0 concept. In V. Ivanov, et al. (Eds.), Advances in design, simulation and manufacturing (pp. 127–134). NewYork: Springer.Google Scholar
  29. 29.
    Frazelle, H. E. (2016). World-class warehousing and material handling (2nd ed.). New York: McGraw-Hill Education.Google Scholar
  30. 30.
    Manzini, R., Accorsi, R., Pattitoni, L., & Regattieri, A. (2011). A supporting decisions platform for the design and optimization of a storage industrial system. In Efficient decision support systemsPractice and challenges in multidisciplinary domains (Vol. 2, p. 64). InTech.
  31. 31.
    Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383–394. Scholar
  32. 32.
    Li, X., Li, D., Wan, J., Vasilakos, A. V., Lai, C.-F., & Wang, S. (2017). A review of industrial wireless networks in the context of industry 4.0. Wireless Networks, 23(1), 23–41. Scholar
  33. 33.
    Sun, Q., Li, S., Zhao, S., Sun, H., Xu, L., & Nallanathan, A. (2017). Industrial wireless sensor networks 2016. International Journal of Distributed Sensor Networks, 13(6), 1–2. Scholar
  34. 34.
    Cho, K., Park, W., Hong, M., Park, G., Cho, W., Jihoon, J., et al. (2015). Analysis of latency performance of bluetooth low energy (BLE) networks. Sensors (Switzerland), 15(1), 59–78. Scholar
  35. 35.
    Aernouts, M., Berkvens, R., Van Vlaenderen, K., & Weyn, M. (2018). Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas. Data, 3(2), 13. Scholar
  36. 36.
    Abuagoub, A. M. A. (2016). An overview of industrial wireless sensor networks. International Journal of Computer Science and Information Technology Research ISSN, 4(4), 68–80.Google Scholar
  37. 37.
    Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2018). Overview of cellular LPWAN technologies for IoT deployment: Sigfox, LoRaWAN, and NB-IoT. In 2018 IEEE international conference on pervasive computing and communications workshops, PerCom workshops 2018 (pp. 197–202).
  38. 38.
    Catherine, I., Tardy, R., Aakvaag, N., Myhre, B., Bahr, R., Catherine, I., & Bahr, R. (2017). Comparison of wireless techniques applied to environmental sensor monitoring.Google Scholar
  39. 39.
    Statista. (2019). Internet of things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions). Retrieved from
  40. 40.
    Ferrández-Pastor, F. J., Mora, H., Jimeno-Morenilla, A., & Volckaert, B. (2018). Deployment of IoT edge and fog computing technologies to develop smart building services. Sustainability (Switzerland), 10(11), 1–23. Scholar
  41. 41.
    Jabbar, S., Khan, M., Silva, B. N., & Han, K. (2018). A REST-based industrial web of things’ framework for smart warehousing. The Journal of Supercomputing, 74(9), 4419–4433. Scholar
  42. 42.
    Periša, M., Sente, R. E., Cvitić, I., & Kolarovszki, P. (2018). Application of innovative smart wearable device in industry 4.0. In Proceedings of the 3rd EAI international conference on management of manufacturing systems (pp. 1–10). EAI.
  43. 43.
    Pereira, T., Barreto, L., & Amaral, A. (2017). Network and information security challenges within industry 4.0 paradigm. Procedia Manufacturing, 13, 1253–1260. Scholar
  44. 44.
    Symantec. (2016). Smarter security for manufacturing in the industry 4. 0 Era (pp. 1–12). Retrieved from
  45. 45.
    Schrecker, S., Soroush, H., Molina, J., LeBlanc, J., Hirsch, F., Buchheit, M., et al. (2016). Industrial internet of things volume G4: Security framework. Industrial Internet Consortium. Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Transport and Traffic SciencesUniversity of ZagrebZagrebCroatia
  2. 2.Faculty of Operation and Economics of Transport and CommunicationsUniversity of ŽilinaŽilinaSlovakia

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