## 3 Experimental Results  
Different from the previous study's dataset [10], the dataset for this research was recreated by Kyungsung University, Department of Electronics Engineering since previous study's dataset does not include global coordinate system's axes information, which is a must to create our proposed model. Figure 6: Structure of the long short-term memory (LSTM) neural network. Although there exists plenty of publicly available datasets [36; 37; 38; 39], none of them includes the global axes' coordinates, which is an obligatory information to create our proposed egocentric coordinate system. For the coding of the proposed system, previous study's C# code [10] was updated with the proposed method's implementation. For this purpose, Microsoft Visual Studio 2019 was chosen as the C# coding editor. Additionally, several external libraries such as Microsoft Kinect SDK 2.0, Vitruvius, and Accord.NET were used. On the other hand, Python 3.8.3 with TensorFlow was also used for the LSTM network performance evaluation. The dataset created for this study contains information regarding 10 number of users who differ in height, weight, and clothing. Each activity for each person was logged twice to create a training set and once to create a test set. This is because the test sequences contain different angle variations of the same activity that are used to judge the accuracy of the proposed system under real-life conditions. RNN-LSTM model was employed on the dataset generated from the proposed coordinate system, which was separated into 70:30 for training and testing for the k-fold cross-validation. For the k-fold cross-validation, k was selected as 10. Since each person runs the activity at a different pace, the number of instances for each activity is different. Number of instances in the training and testing datasets employed in our experiments are listed in Table 3.  
A sample snapshot from experimental environment is demonstrated in Figure 7 as follows. In previous study [10], we have experimented that LSTM's accuracy is dramatically lower than the other classifiers when the dimension reduction is applied to the feature vectors. Although high-dimensional feature vectors can be used in LSTM networks, the training duration may become high and classification speed (frames per second) can be significantly low in real time. In order to \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multicolumn{2}{c}{**Number of Instances**} & \multicolumn{2}{c}{**Number of Instances**} \\ \cline{2-3}  & **(Training Dataset)** & **(Testing Dataset)** \\ \hline Standing & 3355 & 1409 \\ \hline Walking & 3388 & 1570 \\ \hline Clapping & 3619 & 1397 \\ \hline Lifting & 3645 & 1339 \\ \hline Punching & 3476 & 1543 \\ \hline Total & 17,483 & 7258 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of instances in the training and testing datasets. Figure 7: A snapshot from C#-based experimental environment. solve this problem, dimension reduction is an inevitable preprocessing stage to speed up the LSTM network. However, dimension reduction leads to loss of information which causes low accuracy in the classification. In this study, an egocentric coordinate system is presented to boost the performance of LSTM in low-dimensional feature space. For this purpose, in the experimental setup, RNN-LSTM network is employed to recognize five classes of human activities, namely, walking, standing, clapping, punching, and lifting. Several compression methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) are employed to reduce dimension in feature vectors. This is an essential operation to attain real-time processing speed. Besides, the effect of queue size on the performance of LSTM classification is observed with varying values. Table 4 summarizes the experiment's selected parameters as follows. Additionally, in case of employing DCT compression rate of 90% (24 number of features), Figure 8 shows the graph of accuracy for both training and testing cost.