A conference presentation Presented in the Joint 2021 5th International Conference on Imaging, Vision & Pattern Recognition (IVPR) and 10th International Conference on Informatics, Electronics & Vision (ICIEV)

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13. Samples size of each class of the three datasets:
(from innermost to outermost) UCI HAR, WISDM, and KU -HAR
8.7%
8.5% 10.5%
6.4%
3.2%
9%
2.3%
4.8% 8.7%
4.3%
1.5%
1.2% 2.9%
9%
3.8%
3.8% 9.1%
2.2%
31%
38.5%
11.4%
9.4%
16.7%
15%
13.7%
18.9%
17.3%
18.5%
5.2%
4.4%
Stand (0)
Sit (1)
Talk-sit (2)
Talk-stand (3)
Stand-sit (4)
Lay (5)
Lay-stand (6)
Pick (7)
Jump (8)
Push-up (9)
Sit-up (10)
Walk (11)
Walk-back (12)
Walk-circle (13)
Run/Jog (14)
Stair-up (15)
Stair-down (16)
Table-tennis (17)
Class Sample Ratio

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𝑪𝑿 =
𝑥1 𝑥2 𝑥3 ⋯ 𝑥𝑛
𝑥2 𝑥3 𝑥4 ⋯ 𝑥1
⋮
𝑥𝑛
⋮
𝑥1
⋮
𝑥2
⋱
⋯
⋮
𝑥𝑛−1

15. 0.0 0.5 1.0 1.5 2.0 2.5
-0.5
0.0
0.5
Acc
Time
0 10 20 30 40 50 60
-0.5
0.0
0.5
Acc

16. Input
Data
2D
Max-pooling
(3,3)
Batch
Normalization
Dropout
(0.3)
Flatten
Output
Class
2D
Convolution
sigmoid
2D
Convolution
relu
2D
Max-pooling
(3,3)
2D
Convolution
sigmoid
Dropout
(0.3)
Dense
(256,
sigmoid
Dropout
(0.3)
Dense
(128,
relu
Dropout
(0.3)
Dense
(32,
sigmoid
Dense
(num_classes,
softmax

17. 0 25 50 75 100 125 150 175 200
0
10
20
30
40
50
60
70
80
90
100
Accuracy
(%)
Epoch
Train
Test
(169, 98.9903)
97%+
43
96%+
25
95%+
13
94%+
15
98%+
94 Test Accuracy
0 25 50 75 100 125 150 175 200
0
10
20
30
40
50
60
70
80
90
100
Accuracy
(%)
Epoch
Train
Test
(190, 96.53229)
96%+
86
95%+
90
94%+
10
Test Accuracy
0 25 50 75 100 125 150 175 200
0
10
20
30
40
50
60
70
80
90
100
Accuracy
(%)
Epoch
Train
Test
(195, 96.6654)
94%+
32
93%+
11
95%+
74
96%+
29
Test Accuracy

18. Confusion matrices of the best classifications on testing subsets of the three datasets
(a)
(b) (c)
446 16 7 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
8 436 21 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0
6 4 431 2 0 2 2 0 0 0 1 0 0 0 0 1 0 0
0 1 1 458 0 0 0 1 0 0 0 1 0 3 0 2 0 0
0 0 1 2 539 0 0 3 0 0 0 0 0 0 0 0 0 0
1 5 7 0 0 440 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 3 1 1 429 4 0 0 2 0 0 0 0 0 0 0
0 0 0 1 0 0 1 328 0 2 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 1 161 0 0 0 0 0 4 0 0 0
0 0 0 0 0 0 0 2 0 117 0 0 1 0 0 0 0 0
0 0 0 0 0 0 3 0 0 0 248 0 0 0 0 0 0 0
0 0 0 2 0 0 0 0 0 0 0 215 3 0 0 1 0 0
0 0 1 0 0 0 0 0 0 0 0 0 76 0 0 0 2 0
0 0 0 1 0 0 0 0 0 0 0 1 0 61 0 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 143 0 3 3
0 0 0 1 0 0 0 0 0 0 0 2 0 2 0 189 1 4
0 0 0 1 0 0 0 0 0 0 0 0 0 2 1 2 188 1
0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 2 110
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Actual
Class
Predicted Class
431 0 0 0 0 0
0 386 0 0 0 0
0 0 351 0 0 0
0 1 0 431 12 0
0 0 0 12 465 0
0 0 0 1 0 485
11 15 16 1 0 5
5
0
1
16
15
11
Actual
Class
Predicted Class
1758 4 9 6 0 0
3 1414 9 6 0 0
18 13 466 26 0 3
10 11 33 378 1 1
0 0 2 0 236 3
0 0 2 0 0 202
11 14 15 16 1 0
0
1
16
15
14
11
Actual
Class
Predicted Class

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