Surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model
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United States of America
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Apr 30, 2024
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May 7, 2020
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May 7, 2020
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Abstract
A surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model (MSP-CNN model), step 1: collecting torque signals and surface electromyography (sEMG) signals when tightening a bolt; step 2: dividing a range of a transducer by at least two granularities, generating a plurality of torque sub-ranges corresponding to the at least two granularities and labeling the plurality of torque sub-ranges with torque labels; step 3: generating sEMG graphs of the sEMG signals in each time window; step 4: determining the torque labels of each time window under each of the at least two granularities according to the torque sub-ranges that average values of torques fall in; step 5: establishing a sample set; step 6: building a MSP-CNN model, and training parallel independent CNN models with sample datasets; and step 7: inputting the sEMG signals of the operator during assembly into trained MSP-CNN model and identifying assembly torques.
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