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Table 4 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 a new motion from 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

53.42

71.6

68.15

MSE

0.01286

0.00784

0.00879

r2

0.64099

0.71044

0.7245

R2

– 0.05092

0.35933

0.28147

RNNseq

Zs

49.22

71.27

67.39

MSE

0.01401

0.00793

0.009

FNN

Zs

50.39

67.33

64.76

MSE

0.01369

0.00902

0.00973

FNNseq

Zs

37.51

70.99

69.47

MSE

0.01786

0.00829

0.00873

CNN

Zs

47.87

71.63

68.22

MSE

0.01439

0.00783

0.00877

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