Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine In-terfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflects the subjects'intention in moving their limbs.Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches.However,these methods have drawbacks that limit their usability.Different from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the Artificial Neural Networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,impossible.The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals.The presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric study.All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system.The RBFNN has the best performance over the others with a 79.80％mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67％in some joints.Interestingly,the highest accuracy over individual joints is 93.46％,which is achieved via the GRNN.The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.

Wafa Batayneh;Enas Abdulhay;Mohammad Alothman

Jordan University of Science and Technology,Mechanical Engineering Department,Irbid,22110 Jordan;Jordan University of Science and Technology,Biomedical Engineering Department,Irbid,22110 Jordan

数字通信与网络（英文）

[1]Wafa Batayneh;Enas Abdulhay;Mohammad Alothman-.Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics)[J].数字通信与网络（英文）,2022(02):162-173

neuromusculoskeletal

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