Patent ID: 11971319
Assignee: XI'AN JIAOTONG UNIVERSITY
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC A  G

Claim 0:
1. A surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model, comprising following steps:
(Step 1): collecting and saving torque signals and surface electromyography (sEMG) signals of an operator when tightening a bolt on a test bench;
(Step 2): dividing a range of a tightening torque transducer on the test bench according to at least two granularities, generating a plurality of torque sub-ranges corresponding to each of the at least two granularities and labeling each of the plurality of torque sub-ranges with a torque label;
(Step 3): generating sEMG graphs of the sEMG signals with every t seconds a time window;
(Step 4): calculating average values of all the torque signals in each time window, determining the torque label of each time window under each of the at least two granularities according to the torque sub-ranges that the average values of torques fall in;
(Step 5): establishing a sample set comprising sample datasets of at least two granularities, each of the sample datasets of each of the at least two granularities comprises a plurality of sEMG graphs with torque labels;
(Step 6): building a multi-segmentation parallel CNN model, containing at least two parallel independent CNN models, with classification granularities of the sEMG signals in each of the at least two parallel independent CNN models different, and training the at least two parallel independent CNN models with sample datasets with the same granularity; and
(Step 7): inputting the sEMG signals of the operator during assembly into the trained multi-segmentation parallel CNN model and identifying assembly torques.