## 6 Results
### Classification Results  
The results presented in Table 7 highlight notable differences in performance among various classification methods. Overall, CNN-based approaches (STFT and CWT) achieve the highest accuracy for certain problem types, while TCN (EMD) and LSTM (EMD) methods exhibit lower performance under specific conditions. Additionally, variations in problem definitions and frequency settings significantly impact model performance. For the empty/respiration classification task, the CNN (STFT) method outperforms the others, achieving the highest accuracy in both validation (94.17%) and test (95.83%) phases. In contrast, the TCN (EMD) model exhibits the lowest accuracy (validation: 80.06%, test: 79.17%), indicating a weaker capacity for distinguishing between empty and respiration classes. This trend persists across different frequency settings, although the LSTM (EMD) model reaches 100% accuracy in certain conditions (e.g., 332.5 MHz test phase), demonstrating its potential under specific circumstances. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Problem** & **Freq. [MHz]** & **Classifier** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline
1 & Empty-Respiration & 210 & TCN & 0.66 & 0.89 & 0.76 & 0.79 \\  & & & LSTM & 0.81 & 0.9 & 0.86 & 0.87 \\  & & & CNN (STFT) & 0.92 & 1 & 0.96 & 0.96 \\  & & & CNN (CWT) & 0.9 & 0.9 & 0.91 & 0.92 \\ \hline
2 & Empty-Movement & 210 & TCN & 1 & 0.89 & 0.94 & 0.96 \\  & & & LSTM & 1 & 1 & 1 & 1 \\  & & & CNN (STFT) & 1 & 0.92 & 0.96 & 0.96 \\  & & & CNN (CWT) & 1 & 1 & 1 & 1 \\ \hline
3 & Empty-Respiration-Movement & 210 & TCN & 0.89 & 0.89 & 0.89 & 0.89 \\  & & & LSTM & 0.84 & 0.83 & 0.83 & 0.86 \\  & & & CNN (STFT) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) \\ \hline
4 & Empty-Respiration & 332.5 & TCN & 1 & 0.85 & 0.92 & 0.92 \\  & & & LSTM & 1 & 1 & 1 & 1 \\  & & & CNN (STFT) & 1 & 0.92 & 0.96 & 0.96 \\  & & & CNN (CWT) & 1 & 0.91 & 0.95 & 0.95 \\ \hline
5 & Empty-Movement & 332.5 & TCN & 1 & 1 & 1 & 1 \\  & & & LSTM & 0.9 & 0.91 & 0.91 & 0.92 \\  & & & CNN (STFT) & 1 & 1 & 1 & 1 \\ \hline
6 & Empty-Respiration-Movement & 332.5 & TCN & 0.9 & 0.92 & 0.9 & 0.92 \\  & & & LSTM & 0.74 & 0.8 & 0.74 & 0.75 \\  & & & CNN (STFT) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) \\  & & & CNN (CWT) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) \\ \hline
7 & Empty-Respiration & 405 & TCN & 1 & 0.92 & 0.96 & 0.96 \\  & & & LSTM & 1 & 1 & 1 & 1 \\  & & & CNN (STFT) & 0.92 & 0.92 & 0.92 & 0.92 \\  & & & CNN (CWT) & 0.92 & 0.92 & 0.92 & 0.92 \\ \hline
8 & Empty-Movement & 405 & TCN & 1 & 1 & 1 & 1 \\  & & & LSTM & 1 & 1 & 1 & 1 \\  & & & CNN (STFT) & 1 & 1 & 1 & 1 \\  & & & CNN (CWT) & 1 & 1 & 1 & 1 \\ \hline
9 & Empty-Respiration-Movement & 405 & TCN & 0.97 & 0.97 & 0.97 & 0.97 \\  & & & LSTM & 0.84 & 0.75 & 0.76 & 0.78 \\  & & & CNN (STFT) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) \\  & & & CNN (CWT) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) & \({}^{*}\) \\ \hline \hline \end{tabular}  
* The performance metrics could not be established due to the high demand for workstation memory.  
\end{table} 
Table 6: Performance metrics of different DL models across various classification tasks. For the empty/movement classification, both CNN (STFT) and CNN (CWT) achieve perfect accuracy (100%) across validation and test phases. Interestingly, at 405 MHz, all methods reach 100% accuracy, whereas at 210 MHz, the TCN (EMD) model exhibits the lowest validation accuracy (72.98%), while LSTM (EMD) and CNN-based methods maintain superior performance (100%). This discrepancy suggests that some methods struggle with movement detection at lower frequencies but significantly improve at higher frequencies. The empty/respiration/movement classification task, which involves a more complex three-class distinction, reveals a more pronounced difference in model performance. At 405 MHz, the TCN (EMD) model achieves a relatively high test accuracy (97.22%), whereas LSTM (EMD) performs considerably worse (77.78%).