biopatrec toolbox  (MathWorks Inc)

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Summary of datasets utilized in the study, including their key characteristics and specifications.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques: Sampling

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Comparison of gesture classification results based on sEMG data across different methods.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques: Comparison

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Classification performance of hand movement and gesture recognition models under varying noise levels in EMG signals.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques:

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Sample confusion matrices generated from the analysis of datasets using the proposed approach: ( A ) BioPatRec DB1, ( B ) BioPatRec DB3, and ( C ) Mendeley.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques: Generated

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Normalized confusion matrices showing two random results obtained from applying the proposed algorithm to BioPatRec DB2 data.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques:

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Normalized confusion matrix showcasing the classification performance of the proposed structure on the Ninapro DB2 dataset.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques:

Journal: Scientific Reports

Article Title: Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture

doi: 10.1038/s41598-024-82676-1

Figure Lengend Snippet: Comparison of classification model accuracy rates, presented as percentage values. Bold values indicate the highest accuracy achieved for each dataset.

Article Snippet: Compared to other models that used only the lowest number of parameters, our model achieved competitive classification accuracy for Mendeley, BioPatRec DB2, NinaPro DB5, and NinaPro DB4.

Techniques: Comparison

Journal: Journal of NeuroEngineering and Rehabilitation

Article Title: Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

doi: 10.1186/s12984-021-00822-6

Figure Lengend Snippet: Overall system architecture with main components and data flow: BioPatRec was used for classification, marker tracking located the Arm and scene markers, and animation controller displayed the model of the arm (11 different movements) as well as virtual objects placed in the scene. The virtual models were integrated into the real scene recorded by the stereo camera and the final images were projected to the HMD. The details are explained in the text

Article Snippet: These steps were performed using BioPatRec [ ], which is an open-source framework for pattern recognition control running in MATLAB (MathWorks, US).

Techniques: Marker