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A Study on the Optimal Design of Reinforced Learning-Driven Personalized Physical Training Strategies in Physical Education Instruction

Yinghui Jiang

Yinghui Jiang

Department of Physical Education and Research, Lanzhou UniversityLanzhou, China
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Jiang, Yinghui
 und 
Duqian Ding

Duqian Ding

Department of Physical Education and Research, Lanzhou UniversityLanzhou, China
Suche nach diesem Autor auf
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Ding, Duqian
  
21. März 2025
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Online veröffentlicht: 21. März 2025
Eingereicht: 30. Okt. 2024
Akzeptiert: 07. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0601
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© 2025 Yinghui Jiang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Structure of SOM neural network insertion robot
Structure of SOM neural network insertion robot

Figure 2.

Flow diagram of SOM neural network
Flow diagram of SOM neural network

Figure 3.

Model architecture of DPRM-LRL
Model architecture of DPRM-LRL

Figure 4.

The mean of the scores after the group Cluster
The mean of the scores after the group Cluster

Figure 5.

The average of the scores of boys after clustering
The average of the scores of boys after clustering

Figure 6.

mL-1m ave_reward trend chart
mL-1m ave_reward trend chart

Figure 7.

mL-100k ave_reward trend chart
mL-100k ave_reward trend chart

This algorithm and module combination Ave_RMSE enhancement

Lifting amplitude/% KNNBasic KNNWithMeans KNNBaseline SVD
mL-1m This algorithm 56.40 56.68 55.18 54.01
DDPG+RLSTM 51.63 51.94 50.28 49.03
DDPG+LSTM 34.60 35.02 33.11 31.09
DDPG+T_self_attention 37.53 37.93 35.79 34.17
DDPG+self_attention 35.47 35.88 33.67 32.00
mL-100k This algorithm 75.08 74.45 73.67 74.09
DDPG+RLSTM 45.95 44.58 42.90 43.82
DDPG+LSTM 42.15 40.69 38.89 39.87
DDPG+T_self_attention 53.74 52.58 51.14 51.92
DDPG+self_attention 45.13 43.74 42.04 42.96

Number A student and physical training strategy set

Number Degree of recommendation Sort
1 0.668 3
2 0.526 5
3 0.459 6
4 0.745 2
5 0.324 8
6 0.159 10
7 0.569 4
8 0.951 1
9 0.437 7
10 0.266 9

Female group k-means Cluster results

Cluster1 Cluster2 Cluster3 Cluster4 Cluster5
Case number 650 710 4520 895 210
Lung capacity score 85 84 85 78 83
50 meters running score 62 68 73 64 37
Fixed jump 64 65 73 35 27
Preflexion score 76 82 84 75 75
Sit-ups scores 65 18 71 68 28
1000 meters run /800 meter running score 34 67 75 65 24
Health score 65.25 69.26 79.83 68.56 52.13

Comparison of experimental results

Algorithm mL-1m data set mL-100k data set
Ave_RMSE Ave_MAE Ave_RMSE Ave_MAE
This algorithm 0.402 0.231 0.243 0.106
DDPG+RLSTM 0.446 0.258 0.527 0.407
DDPG+LSTM 0.603 0.467 0.564 0.436
DDPG+T_self_attention 0.576 0.398 0.451 0.267
DDPG+self_attention 0.595 0.425 0.535 0.368
KNNBasic 0.922 0.726 0.975 0.774
KNNWithMeans 0.928 0.738 0.951 0.75
KNNBaseline 0.897 0.703 0.923 0.727
SVD 0.875 0.683 0.938 0.74

Man group k-means Cluster results

Cluster1 Cluster2 Cluster3
Case number 1630 2468 1420
Lung capacity score 84 84 83
50 meters running score 81 78 68
Fixed jump 64 65 16
Preflexion score 75 72 63
Sit-ups scores 72 7 5
1000 meters run /800 meter running score 72 63 49
Health score 78.46 68.21 57.44

This algorithm and module combination Ave_MAE enhancement

Lifting amplitude/% KNNBasic KNNWithMeans KNNBaseline SVD
mL-1m This algorithm 68.18 68.70 67.14 66.19
DDPG+RLSTM 64.46 65.04 63.30 62.24
DDPG+LSTM 35.67 36.72 33.57 31.63
DDPG+T_self_attention 45.18 46.07 43.39 41.73
DDPG+self_attention 41.46 42.41 39.54 37.77
mL-100k This algorithm 86.30 85.90 85.42 85.68
DDPG+RLSTM 47.42 45.73 44.02 45.00
DDPG+LSTM 43.67 41.87 40.03 41.08
DDPG+T_self_attention 66.50 64.40 63.27 63.92
DDPG+self_attention 52.45 50.93 49.38 50.27
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