Abstract
An innovation of an accurate model along with low latency for hand gesture identification is still a huge challenge especially for rehabilitation. This research provides a unified solution for such limitations by constructing an efficient system for hand gesture recognition by employing designed Sewing Driving Training based Optimization (SDTO)_Deep Residual Network (DRN). In this work, pre-processing is done using Gaussian filtering, whereas feature selection is carried out based on SDTO. In order to attain efficient results, appropriate features are extracted at the feature extraction phase. The last step is the hand gesture recognition phase, which is accomplished effectively using DRN and the network is efficiently trained using designed SDTO and it is achieved by the integration of STBO and DTBO. The introduced SDTO_DRN model for hand gesture recognition has attained high accuracy of 0.943, TPR of 0.929, TNR of 0.919, PPV of 0.924, and NPV of 0.924.
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