Evaluation of output force at the arm end based on sEMG

• Published in: Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 11; no. 2; pp. 186 - 191
• Main Authors: Lin, Zi, Xiong, Pengwen, He, Kongfei, Zhang, Fahui
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Information Science & Technology 01.04.2019
• Subjects: Back propagation networks; Correlation coefficient; Correlation coefficients; Electromyography; Feature extraction; Machine learning; Neural networks; Root-mean-square errors; Support vector machines
• ISSN: 1674-7070
• DOI: 10.13878/j.cnki.jnuist.2019.02.009

To facilitate accurate control of the output force at the arm end during live working, a prediction method for arm end force based on surface electromyography ( sEMG) and support vector machine regression ( SVR) is proposed.By forcing the handle at the end of the manipulator to provide the reciprocating push and pull movement,the data of the force sensor at handle Fare recorded.Simultaneously,the EMG signals of the arm are synchronously collected by three sets of EMG sensors.The feature extraction from the EMG signal and F are combined as sample set S inthe sample collection. Half of the sample data is randomly selected as the training set for different machine learning methods,and trained using the back propagation, generalized regression, and SVR neural networks. Finally, the trained neural network is used to predict force F in the whole sample set, and the prediction effect of the model is obtained by means of the root mean square error (RMSE) and correlation coefficient.The results show that the predictio