Applying Deep Learning Networks to Identify Optimized Paths in Gymnastic Movement Techniques
, and
Mar 17, 2025
About this article
Published Online: Mar 17, 2025
Received: Oct 10, 2024
Accepted: Feb 01, 2025
DOI: https://doi.org/10.2478/amns-2025-0265
Keywords
© 2025 Dan Mo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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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 |