An adaptive learning method for long-term gesture recognition based on surface electromyography.

Objective.The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recognition for different days is usually referred to as long-term gesture recognition.Approach.To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spatial patterns features and then conduct dimensionality reduction with non-negative matrix factorization, effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training scheme, we select samples with high confidence from unlabeled samples to adaptively updates the model before daily formal use.Main results.We verify the feasibility of our method on a dataset consisting of 30 d of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classification model before daily formal use.Significance.From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.