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Table 3 All architectures (recurrent neural network (RNN), sub-sequenced input recurrent neural network (RNNseq), feedforward neural network (FNN), sub-sequenced input feedforward neural network (FNNseq), convolutional neural network (CNN)) are tested on the same test data set of the separate subject and evaluated by the zero-line score Zs, mean square error MSE, the squared correlation coefficient r2, and the coefficient of determination R2

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

  

General

Fine-tuned

Subject-specific

RNN

Zs

35.52

88.21

85.16

MSE

0.00747

0.00137

0.00172

r2

0.3338

0.86077

0.83648

R2

0.21422

0.85634

0.81915

RNNseq

Zs

41.23

83.36

77.12

MSE

0.00681

0.00193

0.00265

FNN

Zs

36.68

83.83

85.74

MSE

0.00733

0.00187

0.00165

FNNseq

Zs

40.71

87.95

85.67

MSE

0.00714

0.00145

0.00173

CNN

Zs

42.61

80.18

86.56

MSE

0.00665

0.0023

0.00153

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