# Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data
## 6 Performance Evaluation
### Performance Evaluation Comparison by Using the WISDM Dataset  
This section compares the performance of the proposed model and that of a well-known neural network model with those of previous studies. Table 4 compares the HAR performance with that of the previous RNN-based model using the WISDM dataset. The proposed method shows a value of 98.15%, which is higher than the corresponding results of previous studies. However, the proposed method showed a slightly lower performance compared with the structure that serially connected numerous models (CNN-GRU-LSTM); however, the proposed algorithm has a relatively simple and shallow layer structure as a parallel convolutional layer, as shown in Figure 12.  
The use of RNN-based models for HAR can lead to performance degradation due to the issues of exploding and vanishing gradients in back-propagation. Although LSTM and GRU techniques have been introduced to address these issues, the sequential nature of vector inputs allows for the processing of only one sequential data at a time, making it difficult to take advantage of the parallel processing capabilities of GPUs. As a result, training and inference models may experience somewhat slower speeds. However, the algorithm proposed in this paper uses CNN-based methods to overcome these shortcomings. With a relatively simple image encoding method, it can perform HAR with dimensional concepts (such as space and direction) in the CNN model, allowing for the extraction of features that were not previously detectable in time series data. Table 5 compares the proposed method with CNN-based models, including CNN models that use input data that have been expanded into multidimensional data. The proposed method achieved higher performance than previous CNN-based models. In addition, the HAR data were composed in the form of a time series. Thus, the RNN model that used the data change according to time showed a higher performance than the CNN-based models. However, the method proposed in this study uses only a convolutional layer and shows results similar to those of the RNN-based models. This implies that 18 physical activities can be classified even with a relatively simpler eight-layer model. When examining the structure of the comparison models in Table 5, the large-scale models (Inception-V3 with 313 layers, EfficientNet B0 with 233 layers, and Xception with 126 layers) showed an accuracy of 90.27%, while the small-scale models (Multichannel CNN-GRU with 9 layers, CNN with an attention mechanism with 6 layers, CNN with 6 layers) showed a higher accuracy of 95.38% compared to the large-scale models. We attribute this performance to the loss of feature points between classes due to deep-layer operations on the input data. When visualizing time series data in a typical way, such as generating waveform-based visual data such as graphs or histograms, the feature information that can be obtained from the waveform information is limited, and all features will eventually be integrated unless there are clear feature points. This is because the entire waveform can contain similar features. To prove this, we designed a shallow-layer neural model and chose a parallel input structure and method of expanding the dimension of input data to mimic \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Model** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline
[31] & Tri-PSRNN & 96.62 & 94.76 \\
[31] & PSDRNN & 94.01 & 93.06 \\
[32] & LSTM-CNN & - & 95.85 \\
[33] & LSTM-RNN & 95.40 & 96.40 \\
[34] & Single-input CNN-GRU model A & 92.42 & 92.03 \\
[34] & Single-input CNN-GRU model B & 94.50 & 94.71 \\
[34] & Single-input CNN-GRU model C & 92.55 & 92.37 \\
[34] & Multi-input CNN-LSTM & 95.55 & 95.45 \\
[34] & Multi-input CNN-GRU & 97.22 & 97.21 \\
[35] & CNN-GRU-LSTM & 98.52 & 98.51 \\ - & Proposed model & 98.00 & 98.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the proposed model compared with models based on RNN. deep feature information even in shallow layers.