Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/102073
Title: | Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction | Authors: | Mendes, Nuno Ferrer, João Vitorino, João Safeea, Mohammad Neto, Pedro |
Keywords: | Human Robot Interaction; Human Behavior Recognition; Gestures; Segmentation; Accelerometer | Issue Date: | 2017 | Project: | Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE2020 | metadata.degois.publication.title: | Procedia Manufacturing | metadata.degois.publication.volume: | 11 | Abstract: | This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process. | URI: | https://hdl.handle.net/10316/102073 | ISSN: | 23519789 | DOI: | 10.1016/j.promfg.2017.07.156 | Rights: | openAccess |
Appears in Collections: | FCTUC Eng.Mecânica - Artigos em Revistas Internacionais |
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1-s2.0-S2351978917303621-main.pdf | 1.5 MB | Adobe PDF | View/Open |
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