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3D Gesture Recognition and Adaptation for Human–Robot Interaction
- Source :
- IEEE Access, Vol 10, Pp 116485-116513 (2022)
- Publication Year :
- 2022
- Publisher :
- IEEE, 2022.
-
Abstract
- Gesture-based human-robot interaction has been an important area of research in recent years. The primary aspect for the researchers has always been to create a gesture detection system that is insensitive to lighting and backdrop surroundings. This research proposes a 3D gesture recognition and adaption system based on Kinect for human-robot interaction. The framework comprises the following four modules: pointing gesture recognition, 3D dynamic gesture recognition, gesture adaptation, and robot navigation. The proposed dynamic gesture recognition module employs three distinct classifiers: HMM, Multiclass SVM, and CNN. The adaptation module can adapt to new and unrecognized gestures applying semi-supervised self-adaptation or user consent-based adaptation. A graphical user interface (GUI) is built for training and testing the proposed system on the fly. A simple simulator along with two different robot-navigation algorithms are developed to test robot navigation based on the recognized gestures. The framework is trained and tested through a five-fold cross-validation method with a total of 3,600 gesture instances of ten predefined gestures performed by 24 persons (three age categories: Young, Middle-aged, Adult; each with 1,200 gestures). The proposed system achieves a maximum accuracy score of 95.67% with HMM for the Middle-aged category, 92.59% with SVM for the Middle-aged category, and 89.58% with CNN for the Young category in dynamic gesture recognition. Considering all the three age categories, the system achieves average accuracies of 94.61%, 91.95%, and 88.97% in recognizing dynamic gestures with HMM, SVM, and CNN respectively. Moreover, the system recognizes pointing gestures in real-time.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4e3033255b9434c96678cbeeed17f3c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2022.3218679