## 4 Experiments Section
### Experimental Results
#### 4.2.3 Leave-One-Subject-Out Assessment  
The classification outcomes for every test subject of the leave-one-subject-out assessment for each LSTM model and the SVM baseline model are summarized in Table 8. \begin{table}
\begin{tabular}{c c c} \hline
**Stage** & **Hyper-Parameters** & **Selected Values** \\ \hline Data Pre-Processing & Window Size & 5 \\  & Step Size & 1 \\ \hline \multirow{4}{*}{Architecture} & LSTM\_1 Neurons & 100 \\  & LSTM\_2 Neurons & 100 \\  & LSTM\_3 Neurons & 100 \\  & LSTM\_4 Neurons & 100 \\  & Dropout Rate & 0.5 \\ \hline \multirow{4}{*}{Training} & Optimizer & Adam \\  & Batch Size & 32 \\  & Learning Rate & 0.0001 \\ \hline Total Trainable Parameters & & 297,701 \\ \hline \end{tabular}
\end{table} 
Table 6: List of selected hyper-parameters for LSTM-Features-5. \begin{table}
\begin{tabular}{c c c} \hline
**Batch Size** & **LSTM-Raw (AUC)** & **LSTM-Features-1 (AUC)** & **LSTM-Features-5 (AUC)** \\ \hline
16 & 0.92 & 0.86 & 0.93 \\
32 & 0.92 & 0.86 & 0.93 \\
64 & 0.91 & 0.85 & 0.92 \\
128 & 0.91 & 0.85 & 0.91 \\
256 & 0.90 & 0.85 & 0.90 \\ \hline \end{tabular}
\end{table} 
Table 7: Classification results of different batch sizes across all LSTM models. \begin{table}
\begin{tabular}{c c c} \hline
**Stage** & **Hyper-Parameters** & **Selected Values** \\ \hline \multirow{4}{*}{Architecture} & LSTM\_1 Neurons & 100 \\  & LSTM\_2 Neurons & 100 \\  & LSTM\_3 Neurons & 100 \\  & LSTM\_4 Neurons & 100 \\  & Dropout Rate & 0.5 \\ \hline \multirow{2}{*}{Training} & Optimizer & Adam \\  & Batch Size & 32 \\  & Learning Rate & 0.0001 \\ \hline Total Trainable Parameters & & 297,701 \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._LSTM-Raw was the least efficient, as it exhibited an inferior classification performance compared to the SVM baseline, and had the lowest average AUC. Both the LSTM-Features models slightly outperformed the SVM model, with LSTM-Features-5 achieving the highest average AUC of 83%. These results validated the feasibility of utilizing hand-crafted features to guide the learning of a LSTM network into convergence with the limited sample size and subject count. When comparing the individual subject outcomes between LSTM-Features-1 and LSTM-Features-5, we observed that introducing multiple strides during training in LSTM-Features-5 resulted in an AUC improvement ranging from 1% to 7%, leading to a 2% increase in the average AUC performance. This finding confirmed the hypothesis that incorporating multiple strides during training could enhance the performance of the LSTM-Features model. The LSTM-Features-5 model demonstrated the highest level of robustness, as it was able to consistently attain an AUC of over 71% regardless of the selection of subject data excluded from training. Compared to the LSTM model trained with raw acceleration data, the impact of individual differences on the performance of both the LSTM and SVM models was less noticeable when trained with gait features. For completeness, Figure 10 shows the ROC curve of LSTM-Features-5 with one of the test subject iterations that was closest to the average AUC. The area under the ROC curve was 83%. Figures 11 and 12 depict the training process of one of the leave-one-subject-out iterations for LSTM-Features-5. During the network training, the training loss decreased  
Figure 10: The ROC curve of one of the test subject iterations for LSTM-Features-5.