Surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model

US11971319B2 · kind B2 · utility

Assignees

• QINGDAO UNIVERSITY OF TECHNOLOGY · CN
• XI'AN JIAOTONG UNIVERSITY · CN

Inventors

• CHENG JUN CHEN · Shandong, CN
• Kai-Feng Huang · Taoshan, CN
• Dong Li · Shandong, CN
• Shuai Zheng · Berkeley, US
• Jun Ho HONG · Seojong-myeon, KR

Key dates

• Filing date: May 7, 2020
• Grant date: Apr 30, 2024
• Expiry date: Nov 3, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T11/206
• WIPO field: Medical technology
• WIPO sector: Instruments

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.