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sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model
Compared with the scheme directly trained on small sample data, the recognition rate of transfer learning was significantly improved from 60. 35% to 78. 54%, effectively solving the problem of insufficient data, and proving the applicability and advantages of transfer learning in fine gesture recognition tasks for disabled people.
Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network
In this paper, a method for lower limb movements recognition based on recursive feature elimination and backpropagation neural network of support vector machine is proposed.
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