## 4 Results
### Classroom S-T Behavior Recognition and Analysis
#### 4.2.2 Training and Test Results  
We trained one six-classification model and five two-classification models, and the accuracy of the models is shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes No.**} & \multirow{2}{*}{**Scheme Details**} & \multicolumn{6}{c}{**Recognition Accuracy**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**Action**} & \multicolumn{2}{c}{**Head1**} & \multicolumn{2}{c}{**Head2**} & \multicolumn{2}{c}{**Gestures**} & \multicolumn{1}{c}{**Body**} \\ \hline
1 & VGG16 & 0.77 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline
2 & Bone coordinate information+Random forest & 0.83 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline
3 & Manually designed bone features+Random forest & 0.8 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline
4 & VGG16+FPN+P2 Forecast & 0.81 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline
5 & VGG16+FPN+P2-P5 fusion prediction & 0.79 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline
6 & VGG16+Multi-output & 0.78 & 0.86 & 0.78 & 0.91 & 0.87 \\ \hline
7 & VGG16+FPN+CBAM+P2-P5 Separate prediction & 0.81 & 0.87 & **0.8** & 0.9 & 0.85 \\ \hline
8 & VGG16+FPN+Multilayer CBAM & **0.84** & **0.91** & 0.79 & **0.91** & **0.91** \\ \hline
9 & VGG16+FPN+Multilayer CBAM+Bone Model Decision Fusion & 0.84 & 0.91 & 0.79 & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of all schemes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classification Model**} & **Six Categories** & **Listening** & **Listening** & **Listening** & **Listening** & **Listening** \\  & **Categories** & **Discussion** & **Reading** & **Writing** & **Raising Hand** & **Speaking** \\ \hline
**Accuracy Rate** & 82.50\% & 93.90\% & 75.60\% & 80.40\% & 88.40\% & 88.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy of the classification model. Combined with Figure 8, in the S-T recognition task of a real scene, there is interference from desks, chairs, and other students around individuals, so there is a big gap between the accuracy rate of six categories and the accuracy rate of 97.0% of single behavior recognition. Among the two classification models, the recognition rate of listening-discussion is the highest because students turn sideways and turn around during the discussion, which has a large division with the posture of sitting up while listening. Listening-reading has the lowest recognition rate, which may be due to the fact that the head angle is different from that of listening, and the two are easily confused. In addition, the recognition rate of listening-raising hands and listening-speaking is high, but the three behaviors are easy to confuse in practice. This is because the behaviors in the test data are relatively standard, and the situation of sitting posture misconduct often occurs in the actual classroom. We use the VGG16 classification model to recognize the behavior of the detected person class and use S-T behavior discrimination rules to judge S-T behavior. Combining Figure 8 and Table 9, we find that, although there are some misjudgments in the correctly judged pictures, the reasons for the judgment are consistent with the actual situation, as shown in Figure 8(1-3). There are also cases where the judgment results are correct, but the reason is inconsistent with the actual situation, as shown in Figure 8(4). In addition, from the pictures with wrong judgment, we found that listening is easy to be mistaken for raising hands. In Figure 8(5), most middle school students are prone on the table, and in Figure 8(6), middle school students' casual sitting posture and random placement of their hands are the reasons for the wrong recognition. At this time, despite the existence of judgment rules, the final result still appears wrong.