AGRMTS: A virtual aircraft maintenance training system using gesture recognition based on PSO‐BPNN model. (12th September 2021)

• Record Type: Journal Article
• Authors: Yan, Yuling; Zhang, Lijun; Chen, Minye
• Is Part Of: Computer animation and virtual worlds. Volume 33:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-12
• Subjects: BPNN -- gesture recognition -- Laplace kernel -- virtual maintenance; Computer animation -- Periodicals; Visualization -- Periodicals; 006.6
• DOI: 10.1002/cav.2031
• Languages: English
• ISSNs: 1546-4261

Abstract

Abstract: The quality and efficiency of aircraft maintenance are the key to ensure flight safety and on‐time rate, which mainly depend on the techniques and experience of maintenance engineer. Generally, exercises on physical prototypes are used to improve the maintenance capability of engineers, but this will waste a lot of consumables and easily cause safety accidents. With the development of computer technology, maintenance training in a virtual environment has become an advanced and reliable solution. In this paper, a virtual training system of aircraft maintenance based on gesture recognition interaction is established. Leap Motion is used as a sensor to construct a hybrid machine learning gesture recognition model, so as to obtain natural human–computer interaction experience. In the recognition model, the initial weight matrix and the number of hidden layer nodes in the back propagation neural network are jointly optimized by the Particle Swarm Optimization algorithm with self‐adaption inertial weight. This optimization algorithm achieved a recognition rate of 81.26% in the dynamic gesture database constructed in this paper, which is higher than other available algorithms. A preliminary usability evaluation in university classrooms shows that the teaching system in this paper can achieve a better interactive experience. Abstract : A training system for aircraft virtual maintenance using gesture recognition is presented. To improve the effect of gesture recognition, a joint optimization method for the initial weight matrix and the number of hidden layer nodes in the BPNN model is proposed. For the inertia weight in the adopted PSO optimization algorithm, a new formula is constructed to make it adaptive to the gesture recognition process.