## 5 Ablation Study and Discussion
### Effectiveness of the Proposed Modules  
We conducted an ablation study on the proposed model, based on the optimal parameter configurations of the previous model, to evaluate the contributions of the proposed modules. The results of the ablation experiments are shown in Table 4. In each experiment, we removed specific modules from the proposed model. Additionally, we replaced the ablated modules with alternative modules in some experiments for further testing. For example, we replaced the entire HMC with multi-scale convolution and replaced DCS-LSTM with LSTM. We also deleted ACS in ACFF and used the remaining two convolutional layers instead.  
From Table 4, it is evident that HMC significantly contributes to improving recognition. Its ablation leads to about 0.05 performance degradation across datasets. While the ablation of SFS leads to about 0.03 performance degradation. When we replaced the HMC component with a single multi-scale convolution component, the model performance also decreased by about 0.03, illustrating the importance and effectiveness of the hierarchical architecture and suggesting that multi-scale feature maps captured by the multi-scale convolution are effective.  
Regarding DCS-LSTM, replacing it with a standard LSTM network resulted in a performance decrease of about 0.02, indicating that the ACS operation effectively captures contributions from different channels and learns each channel's weights adaptively. When ACS was ablated from the ACFF component, performance decreased by about 0.02, fur  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Opportunity**} & \multicolumn{2}{c}{**PAMAP2**} & \multicolumn{2}{c}{**USC-HAD**} & \multicolumn{2}{c}{**Skoda**} \\ \cline{2-9}  & **F1-Score** & \(\mathbf{\Delta}\) & **F1-Score** & \(\mathbf{\Delta}\) & **F1-Score** & \(\mathbf{\Delta}\) & **F1-Score** & \(\mathbf{\Delta}\) \\ \hline HMA Conv-LSTM & **0.68** & - & **0.91** & - & **0.53** & - & **0.96** & - \\ \hline -SFS & 0.65 & \(-\)0.03 & 0.87 & \(-\)0.04 & 0.51 & \(-\)0.02 & 0.93 & \(-\)0.03 \\ \hline -ACFF & \multirow{2}{*}{0.65} & \(-\)0.03 & 0.89 & \(-\)0.02 & 0.50 & \(-\)0.03 & 0.94 & \(-\)0.02 \\ (+TWO Convolution Layer) & & & & & & & & \\ \hline -DCS-LSTM & \multirow{2}{*}{0.66} & \(-\)0.02 & 0.89 & \(-\)0.02 & 0.51 & \(-\)0.02 & 0.93 & \(-\)0.03 \\ (+LSTM) & & & & & & & & \\ \hline -HMC & \multirow{2}{*}{0.61} & \(-\)0.07 & 0.86 & \(-\)0.05 & 0.48 & \(-\)0.05 & 0.91 & \(-\)0.05 \\ (+Multi-scale Convolution) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results compared with the full HMA Conv-LSTM model (Macro F1-score).  
Figure 7: Performance of different window overlap rates (**a**) and different hierarchical numbers (**b**).  
ther demonstrating ACS operation's effectiveness. In conclusion, all components in our proposed model significantly contribute to its performance, as evidenced by the results of our ablation experiments. In addition, our study also has some limitations. Our model depends on the quality of the sensor signal. If there are a lot of noise or data missing, it may affect the model's performance.