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Ivan Minárik, Gregor Rozinaj, Renata Rybárová, Marek Vančo, Radoslav Vargic
Modern ways of system control are based on the latest technologies and using corresponding hardware. Modern system can be controlled without addition hardware as mouse or keyboard. Users can control or navigate system and application simply by using their hands (gesture navigation), voice (voice navigation) or eyes (eye tracking). Some systems may use brain-computer interface. This module will introduce listed technologies, to help understand basic principles which meet us in our daily lives.
The main goal of the module is to introduce a student to the fundamental of modern ways of system control in different systems or application. The student is clearly acquainted with the base principles of gesture navigation, voice commands navigation, eye tracking, brain-computer interface and recommendation engine.
Gesture navigation, voice command navigation, eye tracking, brain-computer interface, recommendation engine.
- [1] Vančo, Marek; Minárik, Ivan; Rybárová, Renata. Evolution of static gesture recognition. In: Redžúr 2014 proceedings; 8th International Workshop on Multimedia and Signal Processing; 13 May 2014, Dubrovnik, Croatia. Bratislava: Nakladateľstvo STU, 2014, p. 41-44. ISBN 978-80-227-4162-0.
- [2] Rozinaj, Gregor, et al. Extending System Capabilities with Multimodal Control. Acta Polytechnica Hungarica, 2016, 13.4.
- [3] https://medium.com/iotforall/how-gesture-control-will-transform-our-devices-32d4527a6d25
- [4] http://www.thedrive.com/aerial/10674/djis-spark-is-a-hand-gesture-controlled-drone-that-flies-off-your-hand
- [5] https://stfalcon.com/en/blog/post/intuitive-gestures-in-mobile-app-design
- [6] Parrado Rollan, Marina; Posoldová, Alexandra; Rybárová, Renata. Recommendation engine design using Bayesian network for feature inference. In Redžúr 2016, 10th International workshop on multimedia and signal processing. Bratislava, Slovakia. May 24, 2016. 1. ed. Bratislava: Nakladateľstvo STU, 2016, CD-ROM, pp. 57-60. ISBN 978-80-227-4560-4
- [7] Grau, C., Ginhoux, R., Riera, A., Nguyen, T. L., Chauvat, H., Berg, M., … Ruffini, G. (2014). Conscious Brain-to-Brain Communication in Humans Using Non-Invasive Technologies. PLoS ONE, 9(8), e105225. http://doi.org/10.1371/journal.pone.0105225
- [8] Guy V. et al. Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral sclerosis, 2018, Annals of Physical and Rehabilitation Medicine, 61 (1) , pp. 5-11.
- [9] Fernández, R. et al. Review of real brain-controlled wheelchairs, 2016, J. Neural Eng. 13 061001, https://doi.org/10.1088/1741-2560/13/6/061001
- [10] Kosmyna, N., Tarpin-Bernard, F., Bonnefond, N., & Rivet, B. (2016). Feasibility of BCI Control in a Realistic Smart Home Environment. Frontiers in Human Neuroscience, 10, 416. http://doi.org/10.3389/fnhum.2016.00416
- [11] Furness, D., The University of Florida just held the world’s first mind-controlled drone race, available online: https://www.digitaltrends.com/cool-tech/mind-controlled-drone-race-university-of-florida/
- [12] Chen,S., “Forget the Facebook leak’: China is mining data directly from workers’ brains on an industrial scale”, available online: https://www.scmp.com/news/china/society/article/2143899/forget-facebook-leak-china-mining-data-directly-workers-brains
- [13] Chen, A., “Brain-scanning in Chinese factories probably doesn’t work — if it’s happening at all”, published 1.5.2018, available online: https://www.theverge.com/2018/5/1/17306604/china-brain-surveillance-workers-hats-data-eeg-neuroscience
- [14] Martišius, I., Damaševičius, R., “A Prototype SSVEP Based Real Time BCI Gaming System,” Computational Intelligence and Neuroscience, vol. 2016, Article ID 3861425, 15 pages, 2016. https://doi.org/10.1155/2016/3861425.
- [15] Narayanan, A et al., “Toward domain-invariant speech recognition via large scale training,” Google, USA, arXiv:1808.05312, 2018. https://arxiv.org/abs/1808.05312