# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 5 Discussion  
Figure 7 represents the classification results obtained during the model selection (Table 1) in the form of a radar chart. The configurations of CWT are notated as <Mother wavelet> <maximal scale value> (for example, \"Morlet 32\"). value from 0 to 256 can be regarded as the optimal CWT configuration for wearable-based signal classification using DL models. Regarding the network architecture, in addition to the DenseNet121, models with the DenseNet169, DenseNet201, and Xception architectures also produced high results. In contrast, the InceptionV3, InceptionResNetV2, and ResNet-based architectures seem unsuitable for the classification of CWT-transformed wearable sensor signals. Table 4 compares the results obtained with the KU-HAR dataset in recent state-of-the-art works. As can be noticed, the proposed pre-trained model outperformed the majority of state-of-the-art studies that employed the KU-HAR dataset. This fact verifies the effectiveness of the selected model and the potential of the strategy of employing CWT-generated scalograms together with CNNs to address wearable sensor-based HAR classification problems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Accuracy (\%)** & **FI-Score (\%)** \\ \hline
[66] & 89.5 & 80.67 \\
[54] & 89.67 & 87.59 \\
[41] & - & 94.25 \\
[11] & 94.76 & 94.73 \\
[19] & 96.67 & 96.41 \\ Proposed & 97.48 & 97.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results obtained with the KU-HAR dataset in previous studies. Figure 7: A radar chart with the classification results obtained during the model selection. In [66], the authors used the \"vector point of three axes\" and \"absolute distance\" feature selection techniques on the KU-HAR dataset, which significantly reduced the dimension of the input sample. Then, the RF classifier was utilized, resulting in maximal accuracy and F1 values of 89.5% and 80.67%, respectively. Using the proposed dimension reduction techniques resulted in faster learning, but the classification accuracy decreased significantly compared to the original dataset. In [54], the authors aimed to demonstrate the classification capabilities of the proposed KU-HAR dataset. Utilizing the fast Fourier transform (FFT) for feature extraction and the RF algorithm for classification resulted in the recall, precision, and F1 values of 84.7%, 90.7%, and 87.7%, respectively, as well as more explicit clustering on the t-SNE graph. The authors of [41] used an adaptive DL model with a convolutional layer and gated recurrent unit (GRU). The KU-HAR and HHAR datasets were investigated using the inter-domain activity analysis with the Wasserstein metric criteria for the \"activity representatives\" and \"activity followers\" selections. On the KU-HAR dataset, this method yielded an average F1-score of 94.25%; the average classification accuracy was not provided. In [11], the authors used the RF, Gradient Boost, XGBoost, CatBoost, and LightBoost algorithms for the KU-HAR dataset activity classification. The wavelet package transform was used for feature extraction, and the genetic algorithm was used for feature selection. The best results were achieved using the LightBoost classifier and the Haar mother wavelet, yielding accuracy, precision, recall, and an F1 score of 89.98%, 89.96%, 89.98%, and 89.67%, respectively. The authors of [19] employed a sequential deep learning model with 2D convolutional layers. Circular shifting was used to transform the 1D input samples from the KU-HAR dataset into a 2D matrix, which resulted in promising accuracy and the F1 score of 96.67% and 96.41%, respectively. Finally, in this study, the selected CWT-based model with the DenseNet121 architecture, Morlet wavelet, and the scale value from 0 to 256 achieved the highest classification accuracy and an F1 score of 97.48% and 97.52%, respectively. Despite its performance, the proposed approach has certain limitations related to computational resources.