Gesture Scoring Based on Gaussian Distance-Improved DTW

Abstract

The power industry has been dedicated to applying virtual reality (VR) technology to build training systems in virtual environments, enabling personnel to complete skill training in real simulated environments while ensuring their safety. Conventional action scoring systems struggle to provide accurate scores for fine movements. Accurate scoring of fine movements can help workers identify their shortcomings during power operations, thus improving learning efficiency. This is of great significance for training on virtual environment-based power operation. This paper proposes a power operation-orientated VR action evaluation method based on the Gaussian distance-improved dynamic time warping (DTW) algorithm and the temporal convolutional network (TCN) model. First, the adaptive adapter is used to extract one-dimensional features from the three-dimensional data of the data gloves. Then, based on the TCN model, action data with significant discrepancies are filtered out. Finally, the obtained data are input into the Gaussian distance-improved DTW algorithm, where the path size is calculated. Corresponding scoring criteria are established on the basis of the path size to evaluate the actions. The results demonstrate that the VR action evaluation method based on the Gaussian distance-improved DTW algorithm and the TCN model significantly improves the accuracy of evaluating fine movements compared to traditional evaluation algorithms.