Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference
This work addresses the need for efficient subject-specific classifiers in EMG pattern recognition, offering a solution to reduce calibration time for users, though it is incremental in improving transfer learning for this domain.
The paper tackled the problem of reducing data collection burden in EMG-based motion recognition by proposing an inter-subject variance transfer learning method based on Bayesian inference, which achieved accurate classification with limited target data and demonstrated superiority over existing methods in experiments on two EMG datasets.
In electromyogram (EMG)-based motion recognition, a subject-specific classifier is typically trained with sufficient labeled data. However, this process demands extensive data collection over extended periods, burdening the subject. To address this, utilizing information from pre-training on multiple subjects for the training of the target subject could be beneficial. This paper proposes an inter-subject variance transfer learning method based on a Bayesian approach. This method is founded on the simple hypothesis that while the means of EMG features vary greatly across subjects, their variances may exhibit similar patterns. Our approach transfers variance information, acquired through pre-training on multiple source subjects, to a target subject within a Bayesian updating framework, thereby allowing accurate classification using limited target calibration data. A coefficient was also introduced to adjust the amount of information transferred for efficient transfer learning. Experimental evaluations using two EMG datasets demonstrated the effectiveness of our variance transfer strategy and its superiority compared to existing methods.