Electroencephalography; Electromyography; sEMG; hand gesture; TinyML
this is dataset of sEMG. raw data include four columns, which are channel 1, channel 2, channel 3, and the reference channel. corresponding thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling the calculation of similarity between the sample and the model.
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Electromyography (EMG) has limitations in human machine interface due to disturbances like electrode-shift, fatigue, and subject variability. A potential solution to prevent model degradation is to combine multi-modal data such as EMG and electroencephalography (EEG). This study presents an EMG-EEG dataset for enhancing the development of upper-limb assistive rehabilitation devices. The dataset, acquired from thirty-three volunteers without neuromuscular dysfunction or disease using commercial biosensors is easily replicable and deployable.
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