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Table 6 Recurrent neural network (RNN) is separately trained on several parameters angular position (ang), angular velocity (vel), and angular acceleration (acc) from each joint. As well as on the end-effector (EEF) position and orientation of the hand and EEF+ with additional velocity and acceleration. The RNN is tested on the same test data set of the separate subject and evaluated by the zero-line score Zs and mean square error MSE

From: The concepts of muscle activity generation driven by upper limb kinematics

RNN

 

General

Fine-tuned

Subject-specific

All

Zs

35.52

88.21

85.16

MSE

0.00747

0.00137

0.00172

Ang

Zs

28.93

80.16

72.86

MSE

0.01923

0.00823

0.0023

Vel

Zs

27.0

84.72

69.24

MSE

0.00846

0.00177

0.00356

Acc

Zs

5.66

84.08

77.18

MSE

0.01093

0.00184

0.00264

EEF

Zs

39.81

87.57

83.56

MSE

0.00697

0.00144

0.0019

EEF+

Zs

30.41

89.48

86.37

MSE

0.00806

0.00122

0.00158

  1. The results are presented as an average over all channels, with the bold number indicating the highest score