## 4 Experimental Results
### Performance Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(K\) & Number of hidden layers \\ \hline \(N_{k}/N_{out}\) & Feature vector length of the \(k\)th hidden layer/output size \\ \hline \(N_{k}^{(kernel)}\) & Kernel size of the \(k\)th hidden layer \\ \hline \(N_{k}^{(pool)}\) & Pooling factor in the \(k\)th hidden layer \\ \hline \(C_{k}\) & Number of filters in the \(k\)th hidden layer \\ \hline \(conv/pool\) & Set of indices of convolutional/pooling layers \\ \hline \(L\) & Input sequence length \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notation of equations indicating model complexity. First, the comparative evaluation was conducted with the case of applying \"Element-wise Mean\" to determine whether the weighted mean effectively extracts meaningful spatiotemporal features to recognize action by identifying connectivity of time-varying spatial features. Performance evaluation results show that the proposed method for weighted \(FD^{N}\), in all \(N\) measures higher accuracy than \"Element-wise Mean\" and the weighted mean generates spatiotemporal features to recognize action efficiently. When the spatial and temporal features are fused through unweighted \"Element-wise Mean\" of \(FD^{N}\), the spatiotemporal features are generated by relying solely on the number of \(N\), which is the interval of \(FS^{N}\). Since videos are sequential data with time-varying frames, even in the case of considering only the interval of \(FS^{N}\), the connectivity of the time-varying spatial features can be identified. The motion and background information of an object have nonlinear features rather than constant changes such as the interval of \(FS^{N}\), so if frames depend only on extracted intervals, changes in various environmental conditions cannot be considered. Various environmental conditions include changes in camera's viewpoints and influences of external environmental factors such as lighting and obstacles, according to data acquisition environment, and accordingly, the scale and pose of people's appearances change and motion representation changes over time. Therefore, if spatiotemporal features are generated through \"Element-wise Mean,\" the spatiotemporal features cannot be efficiently generated because spatial representation changes according to the above conditions cannot be identified at a specific time. However, considering the change rate of frames according to the interval extracted through the weighted mean, since big changes in spatial representation are weighed with time information at a specific time and spatiotemporal features are generated, the action recognition performance was improved by considering the motion and background information of objects changing over time effectively. In addition, as a result of the performance comparison with weighted mean and LSTM-based models, \"ConvLSTM\" and \"Bi-ConvLSTM\", when extracting more than 30 frames, LSTM-based models predict action with high recognition accuracy of 2-3% or more. However, when 20-30 frames are extracted, we can see that the proposed weighted mean-based model predicts action with high recognition accuracy of 20% or more. In particular, when 20 frames are extracted, the proposed weighted mean-based model and LSTM-based model showed the greatest performance difference, and the action prediction accuracy and testing time for videos in each method when N = 20 are shown in Table 4 where the tests were conducted in Jetson NANO. An LSTM prediction network has been proposed to solve the problem of long input data length and disappearing influence on initial inputs in RNNs, which sequentially receive and process time series data. It controls the amount of information transmitted from hidden layers to input gates and oblivion gates. When extracting a large number of frames from video, the amount of data increases and the extracted frame interval shortens, so it is advantageous to identify time-varying spatial features.