## 3 Experiment Results
### Experiment Setup
With regard to the datasets collected in this paper, the accuracy of the skeleton recognition algorithm is about 93.6% (defined as all skeleton joints being correctly recognized). The accuracy of human body recognition is 98.7% (defined as the human body being correctly extracted, with non-human or missing areas less than 5%). The main interference area comes from the reflective area of the solar panel. In this paper, as we do not consider errors caused by failed pre-detection, we first removed all the error pre-detection data manually. The experiment randomly extracts 70% of the datasets, then expands and enhances the datasets and trains the model. Meanwhile, the remaining 30% of the datasets will be used for validation. The accuracy of the recognition results for top1 and top2 was calculated, and the final experimental results are shown in Table 3. As mentioned earlier, our method adopts two ways: one is to directly use manually defined significant joints as training for different actions, and the other is to use statistically detected significant joints as training for different actions. Overall, the method proposed in this paper outperforms the comparison method in terms of accuracy in identifying specific actions, especially in the difficult action of 'tie', where recognition accuracy has been significantly improved. **Time consumption**: Due to most behavior recognition algorithms being designed for fixed-length image sequence datasets, they cannot be directly applied to online action recognition. In this paper, in order to verify the feasibility of the proposed algorithm in engineering applications, we also designed relevant experiments to test the execution time of the algorithm. Specifically, for continuously collected image sequences, firstly, a human \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Action**} & \multicolumn{2}{c}{**Slowfast [41]**} & \multicolumn{2}{c}{**ST-GCN [43]**} & \multicolumn{2}{c}{**Our Method \({}^{*}\)**} & \multicolumn{2}{c}{**Our Method \({}^{**}\)**} \\  & **Top1 acc** & **Top2 acc** & **Top1 acc** & **Top2 acc** & **Top1 acc** & **Top2 acc** & **Top1 acc** & **Top2 acc** \\ \hline pendulum & 0.67 & 0.88 & **0.76** & 0.89 & 0.73 & 0.88 & 0.75 & **0.92** \\ \hline stay wire & 0.96 & 0.98 & 0.93 & 0.98 & 0.92 & 0.98 & 0.96 & **0.99** \\ \hline threading & 0.90 & **1.00** & **0.92** & 0.99 & 0.89 & 0.99 & 0.91 & 0.99 \\ \hline tie & 0.28 & 0.66 & 0.60 & 0.78 & 0.70 & 0.78 & **0.79** & **0.85** \\ \hline Total (\%) & 70.25 & 88.00 & 80.25 & 91.00 & 81.00 & 90.75 & **85.25** & **93.75** \\ \hline \hline \end{tabular}  
* with human-designed significant joints in Table 2. ** with detected significant joints in Table 2.  
\end{table} 
Table 3: The experiment results. Figure 9: The intermediate results. (**a**) Human skeleton detection; (**b**) human body area detection. pose recognition algorithm is run to detect and extract skeleton poses. Then, a sliding window is used to package the continuous N (N can be taken as 120 frames, 5 s) skeleton postures to extract features as input to the network of the algorithm proposed in this paper. Finally, the proposed GCN prediction thread is processed to obtain the output result. We have calculated the average execution time of each step mentioned above, and the results are shown in Table 4.  
It should be noted that even without detailed optimization of the algorithm in this paper, after a buffering time of about 5 s, it can output action recognition results at an average of more than two times per second continuously, which can essentially meet the needs of online applications.