  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.
