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Applying Deep Learning Networks to Identify Optimized Paths in Gymnastic Movement Techniques

Dan Mo

Dan Mo

College of Sports Arts, Jilin Sport UniversityChangchun, China
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Mo, Dan
, 
Yintong Wang

Yintong Wang

Graduate Office, Jilin Sport UniversityChangchun, China
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Wang, Yintong
 e 
Bowen Zhang

Bowen Zhang

Department of Physical Education, Hebei Software Vocational and Technical CollegeBaoding, China
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Zhang, Bowen
  
17 mar 2025
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Pubblicato online: 17 mar 2025
Ricevuto: 10 ott 2024
Accettato: 01 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0265
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© 2025 Dan Mo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

OpenPose network structure
OpenPose network structure

Figure 2.

OpenPose's extraction of the skeleton network process
OpenPose's extraction of the skeleton network process

Figure 3.

The skeleton diagram for OpenPose acquisition
The skeleton diagram for OpenPose acquisition

Figure 4.

VGG-19 network structure
VGG-19 network structure

Figure 5.

Improved OpenPose-MobileNet-V3 network structure
Improved OpenPose-MobileNet-V3 network structure

Figure 6.

The parameter change curve of the two strategies in the training process
The parameter change curve of the two strategies in the training process

Comparison with other advanced models on NTU RGB+D and Northwestern-UCLA

Model Accuracy (%)
NTU RGB+D Northwestern-UCLA
Lie Group 52.401 51.843
HBRNN-L 56.992 53.287
Part-Aware LSTM 60.291 54.095
ST-LSTM+Trust Gate 61.165 55.855
Two-stream RNN 65.163 61.322
STA-LSTM 65.551 62.935
Ensemble TS-LSTM 66.617 63.712
Deep STGCK 70.017 78.493
Clips+CNN+MTLN 70.263 75.726
ST-NBMIM 70.808 69.986
E1Att-GRU 71.203 86.976
RotClips+MTCNN 71.547 81.304
ST-GCN 83.957 85.936
BGC-LSTM 84.576 87.729
DPRL 86.226 88.190
OpenPose-MobileNet-V3 95.786 94.572

Identification confusion matrix of cosine annealing strategy

Movement 1 2 3 4 5 6 7
1 0.91 0.00 0.01 0.00 0.05 0.00 0.00
2 0.00 0.90 0.03 0.00 0.00 0.03 0.00
3 0.00 0.02 0.93 0.00 0.00 0.00 0.00
4 0.01 0.00 0.02 0.89 0.00 0.02 0.00
5 0.00 0.01 0.00 0.02 0.91 0.00 0.01
6 0.00 0.00 0.00 0.00 0.04 0.96 0.00
7 0.00 0.00 0.00 0.00 0.00 0.03 0.97
8 0.01 0.00 0.00 0.00 0.00 0.00 0.00
9 0.00 0.00 0.00 0.00 0.01 0.00 0.02
10 0.00 0.00 0.00 0.00 0.01 0.00 0.00
11 0.01 0.00 0.00 0.01 0.00 0.00 0.00
12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
13 0.00 0.01 0.00 0.00 0.00 0.00 0.02
14 0.00 0.00 0.00 0.00 0.00 0.00 0.01
8 9 10 11 12 13 14
1 0.01 0.00 0.00 0.01 0.00 0.01 0.00
2 0.00 0.01 0.00 0.00 0.01 0.01 0.01
3 0.00 0.00 0.04 0.01 0.00 0.00 0.00
4 0.00 0.03 0.03 0.00 0.00 0.00 0.00
5 0.05 0.00 0.00 0.00 0.00 0.00 0.00
6 0.00 0.00 0.00 0.00 0.00 0.00 0.00
7 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8 0.95 0.04 0.00 0.00 0.00 0.00 0.00
9 0.04 0.92 0.00 0.00 0.00 0.01 0.00
10 0.00 0.01 0.97 0.00 0.00 0.00 0.01
11 0.00 0.00 0.02 0.95 0.01 0.00 0.00
12 0.00 0.00 0.00 0.00 0.96 0.00 0.04
13 0.00 0.00 0.00 0.00 0.00 0.94 0.03
14 0.01 0.01 0.00 0.02 0.00 0.00 0.95

Identification confusion matrix of improved OpenPose algorithm

Movement 1 2 3 4 5 6 7
1 0.95 0.01 0.01 0.01 0.00 0.00 0.00
2 0.01 0.97 0.00 0.00 0.00 0.00 0.02
3 0.02 0.01 0.94 0.00 0.00 0.02 0.00
4 0.00 0.04 0.03 0.93 0.00 0.00 0.00
5 0.00 0.01 0.00 0.00 0.95 0.00 0.00
6 0.00 0.00 0.00 0.00 0.00 0.95 0.00
7 0.00 0.00 0.00 0.00 0.01 0.01 0.96
8 0.00 0.00 0.00 0.00 0.00 0.00 0.00
9 0.00 0.00 0.00 0.03 0.01 0.00 0.00
10 0.01 0.03 0.01 0.00 0.00 0.00 0.00
11 0.00 0.01 0.00 0.00 0.00 0.00 0.00
12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
13 0.00 0.00 0.00 0.00 0.01 0.00 0.00
14 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8 9 10 11 12 13 14
1 0.00 0.00 0.00 0.00 0.00 0.01 0.00
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
3 0.00 0.00 0.00 0.01 0.00 0.00 0.00
4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 0.00 0.03 0.01 0.00 0.00 0.00 0.00
6 0.01 0.00 0.02 0.00 0.00 0.02 0.00
7 0.00 0.01 0.00 0.00 0.01 0.00 0.00
8 0.96 0.04 0.00 0.00 0.00 0.00 0.00
9 0.00 0.95 0.01 0.00 0.01 0.00 0.00
10 0.01 0.00 0.96 0.00 0.00 0.00 0.01
11 0.00 0.00 0.01 0.98 0.00 0.00 0.00
12 0.00 0.03 0.00 0.00 0.97 0.00 0.00
13 0.00 0.00 0.00 0.00 0.00 0.96 0.03
14 0.00 0.00 0.01 0.00 0.00 0.01 0.98

Identification confusion matrix of OpenPose algorithm

Movement 1 2 3 4 5 6 7
1 0.87 0.01 0.02 0.03 0.00 0.00 0.01
2 0.00 0.89 0.00 0.02 0.03 0.00 0.01
3 0.00 0.04 0.89 0.01 0.00 0.02 0.03
4 0.01 0.02 0.01 0.90 0.00 0.01 0.00
5 0.01 0.02 0.01 0.01 0.88 0.00 0.00
6 0.00 0.00 0.00 0.00 0.01 0.92 0.02
7 0.01 0.02 0.03 0.01 0.02 0.00 0.90
8 0.00 0.00 0.02 0.02 0.00 0.00 0.00
9 0.04 0.01 0.00 0.00 0.00 0.00 0.00
10 0.02 0.00 0.00 0.01 0.00 0.00 0.00
11 0.02 0.01 0.00 0.00 0.01 0.00 0.00
12 0.00 0.00 0.00 0.00 0.04 0.00 0.00
13 0.01 0.03 0.00 0.01 0.00 0.04 0.01
14 0.01 0.01 0.01 0.02 0.00 0.00 0.00
8 9 10 11 12 13 14
1 0.02 0.01 0.01 0.00 0.01 0.01 0.00
2 0.02 0.00 0.01 0.00 0.00 0.01 0.01
3 0.01 0.00 0.00 0.00 0.00 0.00 0.00
4 0.00 0.00 0.02 0.01 0.00 0.01 0.01
5 0.00 0.03 0.00 0.00 0.02 0.01 0.01
6 0.01 0.01 0.02 0.01 0.00 0.00 0.00
7 0.00 0.00 0.00 0.00 0.01 0.00 0.00
8 0.91 0.00 0.01 0.03 0.01 0.00 0.00
9 0.02 0.92 0.00 0.00 0.00 0.01 0.00
10 0.00 0.02 0.89 0.03 0.00 0.00 0.03
11 0.01 0.04 0.03 0.86 0.02 0.00 0.00
12 0.00 0.00 0.01 0.05 0.89 0.00 0.01
13 0.00 0.00 0.01 0.00 0.03 0.85 0.01
14 0.04 0.00 0.02 0.00 0.01 0.00 0.88
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