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Providing Context-Sensitive Mobile Assistance for People with Disabilities in the Workplace

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 776)

Abstract

Recent research has shown that computer-based Assistive Technology (AT) has the potential to support individuals with disabilities in production environments. At the same time, step-by-step instructions enable workers to be successful in their performance of industrial tasks that were formerly difficult to accomplish. We merged these two types of intervention and developed an application running on a mobile device that can assist disabled workers working more independently. In an evaluation study, we investigated how our assistive system affects the task efficiency as well as participants’ subjective evaluation. Results show advantages when using the assistive prototype with regard to users’ task efficiency and subjective evaluations.

Keywords

Assistive technology People with disabilities Human computer interaction Industry 4.0 Inclusion Context-sensitive assistance Step-by-step instructions Production Mobile assistance 

Notes

Acknowledgments

We thank the sheltered work organization “Werkstatt Begatal of Lebenshilfe Detmold e.V.” for participating in the evaluation and proving pedagogical support during the project.

References

  1. 1.
    World Health Organization and World Bank: World Report on Usability (2011)Google Scholar
  2. 2.
    Statistisches Bundesamt: Statistik der schwerbehinderten Menschen (2015)Google Scholar
  3. 3.
    Aktion Mensch: Inklusionsbarometer Arbeit, Bonn (2016)Google Scholar
  4. 4.
    DIHK: Ausbildung 2017 - Ergebnisse einer DIHK Online Unternehmerbefragung. DIHK, Berlin, Brüssel (2017)Google Scholar
  5. 5.
    Zeller, B.: “Neue Ausbildungspotenziale erschließen,” InfoForum Aktuelles aus dem Forschungsinstitut Betriebliche Bild. - online, no. 3/11 (2011)Google Scholar
  6. 6.
    Korn, O., Schmidt, A., Hörz, T.: The potentials of in-situ-projection for augmented workplaces in production. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems on - CHI EA 2013, p. 979 (2013)Google Scholar
  7. 7.
    Korn, O.: Industrial playgrounds: How gamification helps to enrich work for elderly or impaired persons in production. In: Proceedings of the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems – EICS 2012, p. 4 (2012)Google Scholar
  8. 8.
    Leventhal, J.: Assistive devices for people who are blind or have visual impairments. In: Evaluating, Selecting, and Using Appropriate Assistive Technology, pp. 125–143 (1996)Google Scholar
  9. 9.
    Steel, E.J., De Witte, L.P.: Advances in European assistive technology service delivery and recommendations for further improvement. Technol. Disabil. 23(3), 131–138 (2011)Google Scholar
  10. 10.
    Chang, Y.J., Chen, S.F., Da Huang, J.: A kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Res. Dev. Disabil. 32(6), 2566–2570 (2011)CrossRefGoogle Scholar
  11. 11.
    Hersh, M.A., Johnson, M.A.: Assistive Technology for Visually Impareired and Blind People (2008)Google Scholar
  12. 12.
    Simpson, R.C., Levine, S.P., Bell, D.A., Jaros, L.A., Koren, Y., Borenstein, J.: NavChair: an assistive wheelchair navigation system with automatic adaptation. In: Assistive Technology and Artificial Intelligence, pp. 235–255. Springer, Heidelberg (1998)Google Scholar
  13. 13.
    Robinson, L., Brittain, K., Lindsay, S., Jackson, D., Olivier, P.: Keeping in Touch Everyday (KITE) project: developing assistive technologies with people with dementia and their carers to promote independence. Int. Psychogeriatr. 21(3), 494–502 (2009)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y.: Technology and the writing skills of students with learning disabilities. J. Res. Comput. Educ. 32(4), 467–478 (2000)CrossRefGoogle Scholar
  15. 15.
    Sauer, A.L., Parks, A., Heyn, P.C.: Assistive technology effects on the employment outcomes for people with cognitive disabilities: a systematic review. Disabil. Rehabil. Assist. Technol. 5(6), 377–391 (2010)CrossRefGoogle Scholar
  16. 16.
    Gómez, S., Zervas, P., Sampson, D.G., Fabregat, R.: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. J. King Saud Univ. Comput. Inf. Sci. 26(1), 47–61 (2014)Google Scholar
  17. 17.
    Sampath, H., Indurkhya, B., Sivaswamy, J.: A Communication System on Smart Phones and Tablets for Non-verbal Children with Autism, vol. 7383. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. Part 2, pp. 323–330 (2012)Google Scholar
  18. 18.
    Hakobyan, L., Lumsden, J., O’Sullivan, D., Bartlett, H.: Mobile assistive technologies for the visually impaired. Surv. Ophthalmol. 58(6), 513–528 (2013)CrossRefGoogle Scholar
  19. 19.
    Yamagata, J., Coppola, F., Kowtko, M., Joyce, S.: Mobile app development and usability research to help dementia and alzheimer patients. In: 9th Annual Conference on Long Island Systems, Applications and Technology, LISAT 2013 (2013)Google Scholar
  20. 20.
    Gómez, J., Alamán, X., Montoro, G., Juan, C., Plaza, A.: Am ICog – mobile technologies to assist people with cognitive disabilities in the work place. Adv. Distrib. Comput. Artif. Intell. J. 2(1), 9–17 (2011)Google Scholar
  21. 21.
    Aouf, R., Alawneh, A.A., Al Abboud, H., Alwan, M.: Integration of location-based information into mobile learning management system to verify scientific formulas in informal learning environment. In: Proceedings - 2016 International Conference on Engineering and MIS, ICEMIS 2016 (2016)Google Scholar
  22. 22.
    Kollatsch, C., Schumann, M. Klimant, P. Wittstock, V., Putz, M.: Mobile augmented reality based monitoring of assembly lines. In: Procedia CIRP, vol. 23, no. C, pp. 246–251 (2014)CrossRefGoogle Scholar
  23. 23.
    Büttner, S., Mucha, H., Funk, M., Kosch, T., Aehnelt, M., Robert, S., Röcker, C.: The design space of augmented and virtual reality applications for assistive environments in manufacturing. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments – PETRA 2017, pp. 433–440 (2017)Google Scholar
  24. 24.
    Funk, M., Mayer, S., Schmidt, A.: Using in-situ projection to support cognitively impaired workers at the workplace. In: Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility – ASSETS 2015, pp. 185–192 (2015)Google Scholar
  25. 25.
    Funk, M., Bächler, A., Bächler, L., Korn, O., Krieger, C., Heidenreich, T., Schmidt, A.: Comparing projected in-situ feedback at the manual assembly workplace with impaired workers. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments – PETRA 2015, pp. 1–8 (2015)Google Scholar
  26. 26.
    Büttner, S., Funk, M., Sand, O., Röcker, C.: Using head-mounted displays and in-situ projection for assistive systems. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments – PETRA 2016, pp. 1–8 (2016)Google Scholar
  27. 27.
    Aksu, V., Jenderny, S., Kroll, B., Röcker, C.: A Digital Assistance System Providing Step-by-Step Support for People with Disabilities in Production Tasks (2018)Google Scholar
  28. 28.
    Al-Khalifa, H.S.: Utilizing QR code and mobile phones for blinds and visually impaired people, vol. 5105. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 1065–1069 (2008)Google Scholar
  29. 29.
    Tatsumi, H., Murai, Y., Miyakawa, M., Tokumasu, S.: Use of bar code and RFID for the visually impaired in educational environment, pp. 583–588. Springer, Heidelberg (2004)Google Scholar
  30. 30.
    Uzun, V., Bilgin, S.: Evaluation and implementation of QR code identity tag system for healthcare in Turkey. Springerplus 5(1) (2016)Google Scholar
  31. 31.
    Tsai, S.: Wader: a novel wayfinding system with deviation recovery for individuals with cognitive impairments. In: Proceedings of 9th ACM Conference on Computers and Assessibility, pp. 267–268 (2007)Google Scholar
  32. 32.
    Idrees, A., Iqbal, Z., Ishfaq, M.: An efficient indoor navigation technique to find optimal route for blinds using QR codes. In: Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, pp. 690–695 (2015)Google Scholar
  33. 33.
    Andersen, R.S., Damgaard, J.S., Madsen, O., Moeslund, T.B.: Fast calibration of industrial mobile robots to workstations using QR codes. In: 2013 44th International Symposium on Robotics, ISR 2013 (2013)Google Scholar
  34. 34.
    Phumpho, S., Payakkawan, P., Jansri, A., Tongaram, D., Promprayoon, C., Keattipun, P., Ruengpongsrisuck, B., Punnua, C., Areejit, S., Sooraksa, P.: Anti-copy of 2D barcode using multi-encryption technique. In: Proceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Workshops, pp. 707–711 (2014) Google Scholar
  35. 35.
    Eilers, K., Nachreiner, F., Hänecke, K.: Entwicklung und Überprüfung einer Skala zur Erfassung subjektiv erlebter Anstrengung. Z. Arbeitswiss. 40(H. 4), 215–224 (1986)Google Scholar
  36. 36.
    Hurtienne, J., Naumann, A.: QUESI—A Questionnaire for Measuring the Subjective Consequences of Intuitive Use (2010)Google Scholar
  37. 37.
    Richardson, T., Gilbert, S., Holub, J., Thompson, F., MacAllister, A., Radkowski, R., Winer, E., Davies, P., Terry, S.: Fusing self-reported and sensor data from mixed-reality training. I/Itsec 14158, 1–12 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fraunhofer IOSB-INALemgoGermany
  2. 2.inIT - Institute Industrial IT, OWL University of Applied SciencesLemgoGermany

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