Parameters

Training-Validation-Testing (70% - 15% - 15%)

Training-Validation-Testing (50% - 25% - 25%)

SCG

LM

RPROP

SCG

LM

RPROP

Number of Neurons (Input)

6

6

6

6

6

6

Number of Neurons (Hidden)

10

10

10

10

10

10

Number of Neurons (Output)

2

2

2

2

2

2

Learning Coefficient

0.75

0.75

0.75

0.75

0.75

0.75

% of Error

1.90%

1.30%

3.20%

1.30%

8.40%

2.60%

Number of Epochs

14

11

12

17

8

14

Training*

SCG

LM

RP

SCG

LM

NRP

Gradient

0.0064

0.0022

0.043

0.007

0.0037

0.0416

Training Optimization Time (s)

0.00

0.00

0.00

0.00

0.00

0.00

Number of Training Dataset (Fall-Non Fall)

109 (36 - 73)

109 (36 - 73)

109 (36 - 73)

77 (28 - 49)

77 (28 - 49)

77 (28 - 49)

Number of Validation Dataset (Fall-Non Fall)

23 (7 - 16)

23 (7 - 16)

23 (7 - 16)

39 (12 - 27)

39 (12 - 27)

39 (12 - 27)

Number of Test Dataset (Fall-Non Fall)

23 (7 - 16)

23 (7 - 16)

23 (7 - 16)

39 (10 - 29)

39 (10 - 29)

39 (10 - 29)

Number of Misclassification (Training)

0

0

2

0

7

0

Training Dataset CDR

100.00%

100.00%

98.20%

100.00%

90.90%

100.00%

Number of Misclassification (Validation)

3

2

2

2

2

2

Validation Dataset CDR

87.00%

91.30%

91.30%

94.90%

94.90%

94.90%

Number of Misclassification (Test)

0

0

1

0

4

2

Test Dataset CDR

100.00%

100.00%

95.70%

100.00%

89.70%

94.90%

Overall CDR

98.10%

98.70%

96.80%

98.70%

91.60%

97.40%

Specificity

100.0%

100.0%

98.1%

100.0%

87.6%

98.1%

Sensitivity

94.0%

96.0%

94.0%

96.0%

100.0%

96.0%