Fetching the paper…
Reading the bibliography…
This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition.
J. B. Brooke, “Sus: A ’quick and dirty’ usability scale,” 1996. [Online]. Available: https://api.semanticscholar.org/CorpusID:107686571
1996
Earlier work this paper cites.
2006
Earlier work this paper cites.
S. G. Hart, “Nasa-task load index (nasa-tlx); 20 years later,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting , vol. 50, no. 9, pp. 904–908, 2006. [Online]. Available: https://doi.org/10.1177/154193120605000909
2006
Earlier work this paper cites.
B. Laugwitz, T. Held, and M. Schrepp, “Construction and evaluation of a user experience questionnaire,” in HCI and Usability for Education and Work , A. Holzinger, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 63–76
2008
Earlier work this paper cites.
2017
Earlier work this paper cites.
E.-J. Rolley-Parnell, D. Kanoulas, A. Laurenzi, B. Delhaisse, L. Rozo, D. G. Caldwell, and N. G. Tsagarakis, “Bi-manual articulated robot teleoperation using an external rgb-d range sensor,” in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) . IEEE, 2018, pp. 298–304
2018
Earlier work this paper cites.
D. Tezza and M. Andujar, “The state-of-the-art of human-drone interaction: A survey,” in IEEE Access , vol. 7, 01 2019, pp. 1–1
2019
Earlier work this paper cites.
B. Gromov, G. Abbate, L. M. Gambardella, and A. Giusti, “Proximity human-robot interaction using pointing gestures and a wrist-mounted imu,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 8084–8091
2019
Earlier work this paper cites.
N. Zengeler, T. Kopinski, and U. Handmann, “Hand gesture recognition in automotive human–machine interaction using depth cameras,” Sensors , vol. 19, no. 1, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/1/59
2019
Earlier work this paper cites.
L. Labazanova, A. Tleugazy, E. Tsykunov, and D. Tsetserukou, “Swarmglove: A wearable tactile device for navigation of swarm of drones in vr environment,” in Haptic Interaction , H. Kajimoto, D. Lee, S.-Y. Kim, M. Konyo, and K.-U. Kyung, Eds. Singapore: Springer Singapore, 2019, pp. 304–309
2019
Earlier work this paper cites.
J. Akagi, B. Moon, X. Chen, and C. K. Peterson, “Gesture commands for controlling high-level uav behavior,” Jun. 2019. [Online]. Available: https://doi.org/10.1109/icuas.2019.8797743
2019
Earlier work this paper cites.
O. Bjurling, R. Granlund, J. Alfredson, M. Arvola, and T. Ziemke, “Drone swarms in forest firefighting: A local development case study of multi-level human-swarm interaction,” in Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society , Oct. 2020. [Online]. Available: https://doi.org/10.1145/3419249.3421239
2020
Cited alongside, same era.
A. Zacharaki, I. Kostavelis, A. Gasteratos, and I. Dokas, “Safety bounds in human robot interaction: A survey,” in Safety Science , vol. 127, 2020, p. 104667. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925753520300643
2020
Cited alongside, same era.
J. DelPreto and D. Rus, “Plug-and-play gesture control using muscle and motion sensors,” in 2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI) , 2020, pp. 439–448
2020
Cited alongside, same era.
M. Oudah, A. Al-Naji, and J. Chahl, “Hand gesture recognition based on computer vision: A review of techniques,” Journal of Imaging , vol. 6, no. 8, 2020. [Online]. Available: https://www.mdpi.com/2313-433X/6/8/73
D. Rodríguez-Guerra, G. Sorrosal, I. Cabanes, and C. Calleja, “Human-robot interaction review: Challenges and solutions for modern industrial environments,” IEEE Access , vol. 9, pp. 108 557–108 578, 2021
2021
Later among the works it cites.
L. Yu, H. Abuella, M. Z. Islam, J. F. O’Hara, C. Crick, and S. Ekin, “Gesture recognition using reflected visible and infrared lightwave signals,” IEEE Transactions on Human-Machine Systems , vol. 51, no. 1, pp. 44–55, 2021
2021
Later among the works it cites.
P. Parvathy, K. Subramaniam, G. Prasanna Venkatesan, P. Karthikaikumar, J. Varghese, and T. Jayasankar, “Development of hand gesture recognition system using machine learning,” Journal of Ambient Intelligence and Humanized Computing , vol. 12, pp. 6793–6800, 2021
2021
Later among the works it cites.
W. Zhang, J. Wang, and F. Lan, “Dynamic hand gesture recognition based on short-term sampling neural networks,” IEEE/CAA Journal of Automatica Sinica , vol. 8, no. 1, pp. 110–120, 2021
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
T. Mo and P. Sun, “Research on key issues of gesture recognition for artificial intelligence,” in Soft Computing , vol. 24. Springer, 2020, pp. 5795–5803
2020
Cited alongside, same era.
C. Pfeiffer and D. Scaramuzza, “Human-piloted drone racing: Visual processing and control,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3467–3474, 2021
2021
Cited alongside, same era.
D. Tezza, D. Laesker, and M. Andujar, “The learning experience of becoming a fpv drone pilot,” in Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction , 03 2021, pp. 239–241
2021
Cited alongside, same era.
V. Serpiva, E. Karmanova, A. Fedoseev, S. Perminov, and D. Tsetserukou, “Swarmpaint: Human-swarm interaction for trajectory generation and formation control by dnn-based gesture interface,” in Proc. Int. Conf. on Unmanned Aircraft Systems (ICUAS) , 2021, pp. 1055–1062
2021
Cited alongside, same era.
D. Sarma and M. K. Bhuyan, “Methods, databases and recent advancement of vision-based hand gesture recognition for hci systems: A review,” SN Computer Science , vol. 2, no. 6, Aug. 2021. [Online]. Available: https://doi.org/10.1007/s42979-021-00827-x
2021
Cited alongside, same era.
Y. Lim, N. Pongsakornsathien, A. Gardi, R. Sabatini, T. Kistan, N. Ezer, and D. J. Bursch, “Adaptive human-robot interactions for multiple unmanned aerial vehicles,” Robotics , vol. 10, no. 1, 2021. [Online]. Available: https://www.mdpi.com/2218-6581/10/1/12
2021
Cited alongside, same era.
Later among the works it cites.
F. Fink, N. Reinsperger, M. Grünthal, D. Paulicke, and P. Jahn, “Human drone interaction in delivery of medical supplies: A scoping review of experimental studies,” in PLOS ONE , vol. 17, 04 2022, p. e0267664
2022
Later among the works it cites.
E. Dorzhieva, A. Baza, A. Gupta, A. Fedoseev, M. A. Cabrera, E. Karmanova, and D. Tsetserukou, “Dronearchery: Human-drone interaction through augmented reality with haptic feedback and multi-uav collision avoidance driven by deep reinforcement learning,” in 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) . IEEE, 9-12 October, 2022, pp. 270–277
2022
Later among the works it cites.
A. Kaur and S. Bansal, “Deep learning for dynamic hand gesture recognition: Applications, challenges and future scope,” in 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) , 2022, pp. 1–6
2022
Later among the works it cites.
K. Rein, “’like a jedi master.’ gesture control, tech demos, and magic,” Navigationen - Zeitschrift für Medien- und Kulturwissenschaften , vol. 23, no. 1, pp. 62–75, 2023
2023
Later among the works it cites.
E. Nazarova, I. Babataev, N. Weerakkodi, A. Fedoseev, and D. Tsetserukou, “Hyperpalm: Dnn-based hand gesture recognition interface for intelligent communication with quadruped robot in 3d space,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC) , 2022, pp. 2040–2045
2045
Closest in time.
A. Mujahid, M. J. Awan, A. Yasin, M. A. Mohammed, R. Damaševičius, R. Maskeliūnas, and K. H. Abdulkareem, “Real-time hand gesture recognition based on deep learning yolov3 model,” Applied Sciences , vol. 11, no. 9, 2021. [Online]. Available: https://www.mdpi.com/2076-3417/11/9/4164
2076
Closest in time.