sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model

TL;DR

This paper introduces an improved LightGBM-based model for continuous fine-grained gesture recognition using sEMG signals, achieving high accuracy and effective transfer learning for disabled users.

Contribution

It proposes a novel LightGBM-based approach with innovative schemes for continuous gesture recognition and transfer learning, addressing data insufficiency and improving accuracy.

Findings

Recognition rate reached 90.28% on 18 gestures.

Transfer learning improved accuracy from 60.35% to 78.54%.

Model outperformed Bi-ConvGRU in experiments.

Abstract

Surface electromyogram (sEMG), as a bioelectrical signal reflecting the activity of human muscles, has a wide range of applications in the control of prosthetics, human-computer interaction and so on. However, the existing recognition methods are all discrete actions, that is, every time an action is executed, it is necessary to restore the resting state before the next action, and it is unable to effectively recognize the gestures of continuous actions. To solve this problem, this paper proposes an improved fine gesture recognition model based on LightGBM algorithm. A sliding window sample segmentation scheme is adopted to replace active segment detection, and a series of innovative schemes such as improved loss function, Optuna hyperparameter search and Bagging integration are adopted to optimize LightGBM model and realize gesture recognition of continuous active segment signals. In order to verify the effectiveness of the proposed algorithm, we used the NinaproDB7 dataset to design the normal data recognition experiment and the disabled data transfer experiment. The results showed that the recognition rate of the proposed model was 89.72% higher than that of the optimal model Bi-ConvGRU for 18 gesture recognition tasks in the open data set, it reached 90.28%. 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.