Table 2 Benchmark results of different models on primary school posture recognition and secondary school motion analysis, highlighting improvements achieved by the proposed approach across multiple evaluation metrics.

From: Construction of intelligent evaluation model for physical education classroom in primary and secondary schools based on posture estimation and motion recognition

Model

Primary school student posture dataset

Secondary school motion recognition dataset

Accuracy

Recall

F1 Score

AUC

Accuracy

Recall

F1 Score

AUC

I3D32

89.12±0.03

85.47±0.02

86.03±0.02

88.15±0.03

87.61±0.02

84.92±0.02

85.31±0.01

86.75±0.03

SlowFast33

90.25±0.02

86.10±0.03

87.92±0.02

89.34±0.02

88.33±0.03

86.01±0.02

85.94±0.02

87.89±0.02

TSN34

87.86±0.03

84.03±0.02

84.92±0.02

85.26±0.03

86.12±0.02

83.67±0.03

83.40±0.02

84.91±0.02

VideoMAE35

91.±0.02

88.45±0.02

89.18±0.02

90.72±0.02

90.02±0.03

86.94±0.02

87.77±0.02

88.50±0.03

CoViAR36

88.97±0.03

85.32±0.03

86.01±0.02

87.10±0.02

85.95±0.02

82.85±0.02

84.00±0.03

85.41±0.02

TimeSformer37

92.11±0.02

89.20±0.02

88.95±0.03

91.10±0.02

89.87±0.03

87.31±0.02

87.99±0.02

89.00±0.02

Ours

94.98±0.02

91.66±0.02

92.03±0.02

94.25±0.02

95.34±0.02

92.87±0.02

92.45±0.02

94.90±0.02