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Open AccessArticle
Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human–Robot Interaction
by
Sukhan Lee
Sukhan Lee 1,*,
Soojin Lee
Soojin Lee 1 and
Hyunwoo Park
Hyunwoo Park 2
1
Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea
2
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(15), 4850; https://doi.org/10.3390/s24154850 (registering DOI)
Submission received: 14 June 2024
/
Revised: 11 July 2024
/
Accepted: 22 July 2024
/
Published: 25 July 2024
Abstract
For successful human–robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human–robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS.
Share and Cite
MDPI and ACS Style
Lee, S.; Lee, S.; Park, H.
Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human–Robot Interaction. Sensors 2024, 24, 4850.
https://doi.org/10.3390/s24154850
AMA Style
Lee S, Lee S, Park H.
Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human–Robot Interaction. Sensors. 2024; 24(15):4850.
https://doi.org/10.3390/s24154850
Chicago/Turabian Style
Lee, Sukhan, Soojin Lee, and Hyunwoo Park.
2024. "Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human–Robot Interaction" Sensors 24, no. 15: 4850.
https://doi.org/10.3390/s24154850
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