Optimization of Wushu Sanshou Technical Movement Recognition and Training Based on Machine Learning Algorithm
19. März 2025
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Online veröffentlicht: 19. März 2025
Eingereicht: 13. Nov. 2024
Akzeptiert: 15. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0508
Schlüsselwörter
© 2025 Yao Shang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1.

The initial parameter Settings of the action recognition model of martial arts
Parameter | Set value |
---|---|
Learning strategy | Step |
Initial learning rate | 0.00001 |
Batch size | 6 |
Optimizer | Adam |
Iteration number | 500 |
The center of the pressure center is the maximum offset (cm)
Experimental group | Control group | p | |||
---|---|---|---|---|---|
Intergroup comparison | Pretraining | 3.26 ± 0.75 | 3.34 ± 1.23 | 0.211 | |
After training | 3.75 ± 0.31 | 3.52 ±0.68 | 0.034 |
The maximum offset in the direction of the pressure center (cm)
Experimental group | Control group | p | ||
---|---|---|---|---|
Intergroup comparison | Pretraining | 10.21 ± 5.63 | 11.04 ± 4.57 | 0.334 |
After training | 13.58 ± 3.17 | 12.55 ± 3.24 | 0.025 |
The vertical direction is compared to the stable time group(msec)
Experimental group | Control group | p | ||
---|---|---|---|---|
Intergroup comparison | Pretraining | 796.65 ± 163.21 | 623.51 ± 255.18 | 0.14 |
After training | 701.52±211.63 | 773.09±304.21 | 0.037 |
Comparison between front and rear to the stable time group (msec)
Pretraining | After training | p | ||
---|---|---|---|---|
Group comparison | Experimental group | 1262.32 ± 502.31 | 1426.78 ± 336.29 | 0.012 |
Control group | 1265.07 ± 412.25 | 1323.16 ± 362.68 | 0.053 |
The vertical direction is compared in the stable time group(msec)
Pretraining | After training | p | ||
---|---|---|---|---|
Group comparison | Experimental group | 796.65 ± 163.21 | 701.52±211.63 | 0.003 |
Control group | 623.51 ± 255.18 | 773.09±304.21 | 0.109 |
Experimental environment configuration
Entry | Configuring |
---|---|
CPU | Intel® Core™ i7-9750H @2.60GHz |
Memory | 64.0GB |
Python | 3.6 |
Pytorch | 1.9.1 |
The front and rear are to the arrival of stable time statistics (msec)
Experimental group | Control group | p | ||
---|---|---|---|---|
Intergroup comparison | Pretraining | 1262.32 ± 502.31 | 1265.07 ± 412.25 | 0.212 |
After training | 1426.78 ± 336.29 | 1323.16 ± 362.68 | 0.007 |
The comparison of the maximum offset in front of the pressure center (cm)
Pretraining | After training | p | ||
---|---|---|---|---|
Group comparison | Experimental group | 3.26 ± 0.75 | 3.75 ± 0.31 | 0.018 |
Control group | 3.34 ± 1.23 | 3.52 ±0.68 | 0.175 |
The center of the pressure center is compared to the maximum offset group(cm)
Pretraining | After training | p | ||
---|---|---|---|---|
Group comparison | Experimental group | 10.21 ± 5.63 | 13.58 ± 3.17 | 0.012 |
Control group | 11.04 ± 4.57 | 12.55 ± 3.24 | 0.163 |
Experimental results
Action classification method | m AP | GFLOPs/V |
---|---|---|
ST-GCN | 87.63% | 55.17 |
AGCN | 89.01% | 54.32 |
PoseC3D | 93.54% | 36.85 |
MS-G3D | 90.37% | 42.01 |
OURS | 95.22% | 29.35 |
The comparison of the total offset in the pressure center (cm)
Pretraining | After training | p | ||
---|---|---|---|---|
Group comparison | Experimental group | 143.06 ± 12.21 | 154.17 ± 25.04 | 0.038 |
Control group | 141.08 ± 22.67 | 141.93 ± 25.81 | 0.535 |