Actor-Critic Reinforcement Learning Based Algorithm for Contaminant Minimization in sEMG Movement Recognition.

This paper aims to present an approach based on Reinforcement Learning (RL) concept to detect contaminants' type and minimize their effect on surface electromyography signal (sEMG) based movement recognition. The referred method was applied in the pre-processing stage of a sEMG based motion classification system using the Ninapro database 2 artificially contaminated with electrocardiography (ECG) interference, motion artifact (MOA), powerline interference (PLI) and additive white Gaussian noise (WGN). Support Vector Machine was the method for movement classification. The results showed an improvement of 8.9%, 16.7%, 15.9%, 16.5%, and 11.9% in the movement recognition accuracy with the application of the pre-processing algorithm to restore, respectively, one, three, six, nine, and 12 contaminated channels.

Actor-Critic Reinforcement Learning Based Algorithm for Contaminant Type Identification in Surface Electromyography Data.

This paper aims to present an innovative approach based on Reinforcement Learning (RL) concept to detect contaminants' type and minimize their effect on surface electromyography signal (sEMG). An agent-environment model was created based on the following elements: environment (muscle electrical activity), state (set of six features extracted from the signal), actions (application of filters/procedures to reduce the impact of each interference), and agent (controller, which will identify the type of contamination and take the appropriate action). The learning was conducted with Actor-Critic method. An average accuracy of 92.96% was achieved in an off-line experiment when detecting four contaminant types (electrocardiography (ECG) interference, movement artifact, power line interference, and additive white Gaussian noise).

Genetic Algorithm Application to Feature Selection in sEMG Movement Recognition with Regularized Extreme Learning Machine.

This paper presents a genetic algorithm (GA) feature selection strategy for sEMG hand-arm movement prediction. The proposed approach evaluates the best feature set for each channel independently. Regularized Extreme Learning Machine was used for the classification stage. The proposed procedure was tested and analyzed applying Ninapro database 2, exercise B. Eleven time domain and two frequency domain metrics were considered in the feature population, totalizing 156 combined feature/channel. As compared to previous studies, our results are promising - 87.7% accuracy was achieved with an average of 43 combined feature/channel selection.

Recurrent Neural Network for Contaminant Type Detector in Surface Electromyography Signals.

A surface Electromyography (sEMG) contaminant type detector has been developed by using a Recurrent Neural Network (RNN) with Long Short-Term (LSMT) units in its hidden layer. This setup may reduce the contamination detection processing time since there is no need for feature extraction so that the classification occurs directly from the sEMG signal. The publicly available NINAPro (Non-Invasive Adaptive Prosthetics) database sEMG signals was used to train and test the network. Signals were contaminated with White Gaussian Noise, Movement Artifact, ECG and Power Line Interference. Two out of the 40 healthy subjects' data were considered to train the network and the other 38 to test it. Twelve models were trained under a -20dB contamination, one for each channel. ANOVA results showed that the training channel could affect the classification accuracy if SNR = -20dB and 0dB. An overall accuracy of 97.72% has been achieved by one of the models.

sEMG feature selection and classification using SVM-RFE.

It is challenging to obtain good results for hand movements classification. Previous studies expended efforts on filters for sEMG data, feature extraction and classifier algorithms to achieve the best results. This paper proposes the insertion of a step in the classification process that selects which features to use in training aiming to increase accuracy and performance. Feature selection was previously used in other classification tasks but is new in wrist/fingers movements classification. Obtained results were positives as the performance gain is huge (39 to 53 features out of 144 are used for classification) and accuracy reach promising values (above 90% for some subjects).