## 3 Experimental Results and Analysis
### Evaluation of Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & **Precision** & **Recall** & **F1-Score** \\ \hline Clap & 1.00 & 0.89 & 0.94 \\ Hit\_bolt & 0.33 & 0.29 & 0.31 \\ Hit\_stick & 0.59 & 0.68 & 0.64 \\ Jog\_fb & 0.67 & 0.61 & 0.63 \\ Jog\_side & 0.85 & 0.58 & 0.69 \\ Kick & 0.99 & 0.85 & 0.92 \\ Punch & 0.83 & 0.95 & 0.84 \\ Run\_fb & 0.28 & 0.33 & 0.30 \\ Run\_side & 0.45 & 0.77 & 0.57 \\ Stab & 0.37 & 0.39 & 0.38 \\ Walk\_fb & 0.91 & 0.95 & 0.93 \\ Walk\_side & 0.96 & 1.00 & 0.98 \\ Wave\_hands & 1.00 & 1.00 & 1.00 \\
**Average** & **0.71** & **0.71** & **0.70** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of the proposed method on all action types based on precision, recall, and F1-score on Split 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Action** & **Precision** & **Recall** & **F1-Score** \\ \hline Kick & 0.99 & 0.92 & 1.00 \\ Punch & 0.76 & 0.99 & 0.83 \\ Run\_fb & 0.73 & 0.53 & 0.62 \\ Run\_side & 1.00 & 0.76 & 0.86 \\ Stab & 0.40 & 0.55 & 0.46 \\ Walk\_fb & 1.00 & 1.00 & 1.00 \\ Walk\_side & 0.98 & 0.98 & 0.98 \\ Wave\_hands & 0.97 & 1.00 & 0.99 \\
**Average** & **0.83** & **0.83** & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 6: Confusion matrix for Split 1. On the other hand, some actions demonstrated lower precision, recall, and F1-score values. For example, the \"Hit_bott\" action achieved lower scores on all the three splits, with the lowest F1-score being 0.16 on Split 1. Similarly, the \"Stab\" action had an F1-score of 0.34 on Split 1, 0.46 on Split 2, and 0.38 on Split 3. The lower performance for these actions (Hit_bott, Stab) could be attributed to the higher complexity of the movements and the similarity of these actions with each other and some other classes, making it challenging for the proposed framework to differentiate them from others. Moreover, factors, such as background clutter and variation in viewpoint, could further hinder the recognition of these actions. It is worth mentioning that there is performance variation for some actions across different splits. For instance, the \"Hit_stick\" action had an F1-score of 0.65 on Split 1, which increased to 0.75 on Split 2 and then decreased slightly to 0.64 in Split 3. This observation suggests that the performance of the proposed framework is sensitive to the choice of training and testing data. We also calculated the computational performance of the proposed method. The evaluation was performed in terms of the number of network parameters (in millions) and the number of floating-point operations (FLOPS) (in millions) and the classification time for the proposed customized ConvLSTM network. We practically implemented this model on Intel(R) Core(TM) i5-8250U CPU @ 1.80 GHz with 8.00 GB RAM. The total number of FLOPS was 36.79 million, with 1.03 million trainable parameters. The classification time for the 612 test sequences with 30 frames each on Split 1 was 3.58 s. The per sequence classification time was 5.457 milliseconds. This suggests that the proposed method is lightweight in terms of computational complexity and could be deployable in real-world applications.