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Future Directions

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Human Movements in Human-Computer Interaction (HCI)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 996))

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Abstract

What does the future hold for motion-based interaction methods? Will increasingly popular concepts and inventions, such as immersive virtual reality, hasten their already rapid development? Or will they be supplanted by other solutions, such as interfaces capable of reading brain activity directly, or those that recognize voice commands? Neither of these require movement. Each enables new perspectives for both users and technology, but also carry many risks. The development of technologies related to the recording of movement, such as immersive virtual reality, is also of high importance for other branches of science, including psychology. To date, no solution has facilitated the analysis of human behaviors by psychologists in such a detailed manner, nor in such natural environments.

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References

  1. Nicolelis, M.A.L.: Brain–machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci. 4, 417–422 (2003)

    Article  Google Scholar 

  2. Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8, 164–173 (2000)

    Article  Google Scholar 

  3. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S., Thakor, N.V.: A brain-computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4, 40 (2007)

    Article  Google Scholar 

  4. Miladinović, A., Ajčević, M., Battaglini, P.P., Silveri, G., Ciacchi, G., Morra, G., Jarmolowska, J., Accardo, A.: Slow cortical potential BCI classification using sparse variational bayesian logistic regression with automatic relevance determination. In: XV Mediterranean Conference on Medical and Biological Engineering and Computing—MEDICON 2019, pp. 1853–1860. Springer International Publishing (2020)

    Google Scholar 

  5. İşcan, Z., Nikulin, V.V.: Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations. PLoS One. 13, e0191673 (2018)

    Google Scholar 

  6. de Vries, S., Mulder, T.: Motor imagery and stroke rehabilitation: a critical discussion. J. Rehabil. Med. 39, 5–13 (2007)

    Article  Google Scholar 

  7. Llanos, C., Rodriguez, M., Rodriguez-Sabate, C., Morales, I., Sabate, M.: Mu-rhythm changes during the planning of motor and motor imagery actions. Neuropsychologia 51, 1019–1026 (2013)

    Article  Google Scholar 

  8. Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 566–576 (2017)

    Article  Google Scholar 

  9. Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci. 2014, 730218 (2014)

    Google Scholar 

  10. Sagee, G.S., Hema, S.: EEG feature extraction and classification in multiclass multiuser motor imagery brain computer interface u sing Bayesian network and ANN. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 938–943 (2017)

    Google Scholar 

  11. Chang, A.Y., Skirbekk, V.F., Tyrovolas, S., Kassebaum, N.J., Dieleman, J.L.: Measuring population ageing: an analysis of the global burden of disease study 2017. Lancet Public Health. 4, e159–e167 (2019)

    Article  Google Scholar 

  12. Belkacem, A.N., Jamil, N., Palmer, J.A., Ouhbi, S., Chen, C.: Brain computer interfaces for improving the quality of life of older adults and elderly patients. Front. Neurosci. 14, 692 (2020)

    Article  Google Scholar 

  13. Foong, R., Ang, K.K., Quek, C., Guan, C., Phua, K.S., Kuah, C.W.K., Deshmukh, V.A., Yam, L.H.L., Rajeswaran, D.K., Tang, N., Chew, E., Chua, K.S.G.: Assessment of the efficacy of EEG-based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Trans. Biomed. Eng. 67, 786–795 (2020)

    Article  Google Scholar 

  14. Herweg, A., Gutzeit, J., Kleih, S., Kübler, A.: Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol. Psychol. 121, 117–124 (2016)

    Article  Google Scholar 

  15. Schroeder, J., Epley, N.: Mistaking minds and machines: how speech affects dehumanization and anthropomorphism. J. Exp. Psychol. Gen. 145, 1427–1437 (2016)

    Article  Google Scholar 

  16. Callaway, C., Sima’an, K.: Wired for speech: how voice activates and advances the human-computer relationship. Comput. Linguist. Assoc. Comput. Linguist. 32, 451–452 (2006)

    Google Scholar 

  17. Druga, S., Williams, R., Breazeal, C., Resnick, M.: “Hey google is it ok if I eat you?” In: Proceedings of the 2017 Conference on Interaction Design and Children—IDC ’17 (2017). https://doi.org/10.1145/3078072.3084330

  18. Reid Chassiakos, Y.L., Radesky, J., Christakis, D., Moreno, M.A., Cross, C.: Council on communications and media: children and adolescents and digital media. Pediatrics 138, (2016). https://doi.org/10.1542/peds.2016-2593

  19. De La Bastide, D.: With robots in the future. https://interestingengineering.com/research-says-kids-will-be-bffs-with-robots-in-the-future

  20. Yarosh, S., Thompson, S., Watson, K., Chase, A., Senthilkumar, A., Yuan, Y., Brush, A.J.B.: Children asking questions: speech interface reformulations and personification preferences. In: Proceedings of the 17th ACM Conference on Interaction Design and Children, pp. 300–312. Association for Computing Machinery, New York, NY, USA (2018)

    Google Scholar 

  21. Newman, J.: To siri with love: a mother, her autistic son, and the kindness of machines. Harper (2018)

    Google Scholar 

  22. Porcheron, M., Fischer, J.E., Reeves, S., Sharples, S.: Voice interfaces in everyday life. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12. Association for Computing Machinery, New York, NY, USA (2018)

    Google Scholar 

  23. Skorupska, K., Nunez, M., Kopec, W., Nielek, R.: Older adults and crowdsourcing: android tv app for evaluating TEDx subtitle quality. Proc. ACM Hum. Comput. Interact. 2, 1–23 (2018)

    Google Scholar 

  24. Kopeć, W., Skibiński, M., Biele, C., Skorupska, K.: Hybrid approach to automation, RPA and machine learning: a method for the human-centered design of software robots. arXiv preprint arXiv. (2018)

    Google Scholar 

  25. Greene, J.: Solving the trolley problem. Companion Exp. Philos. 175–178 (2016)

    Google Scholar 

  26. Flessert, M., Beran, M.J.: Delayed gratification. In: Encyclopedia of Animal Cognition and Behavior, pp. 1–7 (2018)

    Google Scholar 

  27. Pan, X., Hamilton, A.F. De C.: Why and how to use virtual reality to study human social interaction: the challenges of exploring a new research landscape. Br. J. Psychol. 109, 395–417 (2018)

    Google Scholar 

  28. Slater, M.: Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 3549–3557 (2009)

    Google Scholar 

  29. Martens, M.A., Antley, A., Freeman, D., Slater, M., Harrison, P.J., Tunbridge, E.M.: It feels real: physiological responses to a stressful virtual reality environment and its impact on working memory. J. Psychopharmacol. 33, 1264–1273 (2019)

    Article  Google Scholar 

  30. Marín-Morales, J., Higuera-Trujillo, J.L., Greco, A., Guixeres, J., Llinares, C., Gentili, C., Scilingo, E.P., Alcañiz, M., Valenza, G.: Real versus immersive-virtual emotional experience: analysis of psycho-physiological patterns in a free exploration of an art museum. PLoS One. 14, e0223881 (2019)

    Google Scholar 

  31. Yee, N., Bailenson, J.: The proteus effect: the effect of transformed self-representation on behavior. Hum. Commun. Res. 33, 271–290 (2007)

    Article  Google Scholar 

  32. Aardema, F., O’Connor, K., Côté, S., Taillon, A.: Virtual reality induces dissociation and lowers sense of presence in objective reality. Cyberpsychol. Behav. Soc. Netw. 13, 429–435 (2010)

    Article  Google Scholar 

  33. Lange, B., Pauli, P.: Social anxiety changes the way we move—a social approach-avoidance task in a virtual reality CAVE system. PLoS One. 14, e0226805 (2019)

    Google Scholar 

  34. Baker, C., Pawling, R., Fairclough, S.: Assessment of threat and negativity bias in virtual reality. Sci. Rep. 10, 17338 (2020)

    Article  Google Scholar 

  35. Francis, K.B., Terbeck, S., Briazu, R.A., Haines, A., Gummerum, M., Ganis, G., Howard, I.S.: Simulating moral actions: an investigation of personal force in virtual moral dilemmas. Sci. Rep. 7, 13954 (2017)

    Article  Google Scholar 

  36. Nichols, S.: Virtual reality induced symptoms and effects (VRISE). Methodological and the Theoretical Issues. University of Nottingham (1999)

    Google Scholar 

  37. Sharples, S., Cobb, S., Moody, A., Wilson, J.R.: Virtual reality induced symptoms and effects (VRISE): comparison of head mounted display (HMD), desktop and projection display systems. Displays 29, 58–69 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. National Information Processing Institute, National Research Institute, Warsaw, Poland

    Cezary Biele

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  1. Cezary Biele
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Correspondence to Cezary Biele .

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Biele, C. (2022). Future Directions. In: Human Movements in Human-Computer Interaction (HCI). Studies in Computational Intelligence, vol 996. Springer, Cham. https://doi.org/10.1007/978-3-030-90004-5_11

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