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• W4313503779 abstract "The definition of human-computer interaction (HCI) has changed in the current year because people are interested in their various ergonomic devices ways. Many researchers have been working to develop a hand gesture recognition system with a kinetic sensor-based dataset, but their performance accuracy is not satisfactory. In our work, we proposed a multistage spatial attention-based neural network for hand gesture recognition to overcome the challenges. We included three stages in the proposed model where each stage is inherited the CNN; where we first apply a feature extractor and a spatial attention module by using self-attention from the original dataset and then multiply the feature vector with the attention map to highlight effective features of the dataset. Then, we explored features concatenated with the original dataset for obtaining modality feature embedding. In the same way, we generated a feature vector and attention map in the second stage with the feature extraction architecture and self-attention technique. After multiplying the attention map and features, we produced the final feature, which feeds into the third stage, a classification module to predict the label of the correspondent hand gesture. Our model achieved 99.67%, 99.75%, and 99.46% accuracy for the senz3D, Kinematic, and NTU datasets." @default.
• W4313503779 created "2023-01-06" @default.
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• W4313503779 date "2023-01-05" @default.
• W4313503779 modified "2023-09-30" @default.
• W4313503779 title "Multistage Spatial Attention-Based Neural Network for Hand Gesture Recognition" @default.
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• W4313503779 doi "https://doi.org/10.3390/computers12010013" @default.
• W4313503779 hasPublicationYear "2023" @default.
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