Table 1: Classification test accuracy results.

Test 1 only uses time-domain features.

In test 2, only frequency domain features are added.

In test 3, time-frequency-domain features are added.

In test 4, only the IEMG feature is used.

Test 5, only time-domain features are used, and

no loads are considered in classification.

Type of

model

Kernel

function

Test 1

Accuracy

[%]

Test 2

Accuracy

[%]

Test 3

Accuracy

[%]

Test 4

Accuracy

[%]

Test 5

Accuracy

[%]

DT

Fine tree

73.8

54.4

86.4

64

88.5

DT

Medium tree

62.3

45.7

69.2

52

79.1

DT

Coarse tree

38.8

30.4

43

30.8

63.3

SVM

Linear SVM

84.2

26.7

88.5

67.3

85.8

SVM

Quadratic SVM

98.4

19.4

98.3

84.9

97.5

SVM

Fine Gaussian

99.8

50.7

99.7

99.5

99.3

SVM

Medium Gaussian

95.9

44

96.8

90.5

94.4

SVM

Coarse Gaussian

81.2

37

88

75.6

86.4

KNN

Fine KNN

99.8

71.3

99.8

99.8

99.8

KNN

Medium KNN

99.7

71.3

99.8

99.5

95.7

KNN

Coarse KNN

94.4

62.6

95.7

90.2

95.7

KNN

Cosine KNN

99.8

37.8

99.8

99.6

99.8

KNN

Weighted KNN

99.8

71.9

99.8

99.8

99.8

Ensemble

Boosted Trees

66.2

47.6

83.9

56.9

84.7

Ensemble

Bagged Trees

99.8

76.9

99.8

99.8

99.8

Ensemble

Subspace Discriminator

57.1

23.6

57.1

45.3

73

Ensemble

RUsBoosted Trees

63.9

44.3

83.6

57.1

78.7

Number

of classes

 

14

14

14

14

6

DTs = Decision trees.

SVM = support vector machines.

KNN = k-Nearest Neighbours.