## 2 Materials and Methods
### Machine Learning Classifiers  
In this work, we compared five popular classifiers: random forest, J48, JRip, naive Bayes, and decision table. These classifiers perform well on small datasets, are robust to overfitting, and have been utilized by prior work on human gait recognition and alcohol \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Feature Name**} & \multicolumn{4}{c}{**Before Normalization**} & \multicolumn{4}{c}{**After Normalization**} & \multirow{2}{*}{**Coef Diff**} \\ \cline{3-3} \cline{5-8}  & & **Coef** & \(p\)**-Value** & \
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef** & \(p\)**-Value** &
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef Diff** \\ \hline
7 & Standard Deviation (\(\sigma\)) & \(-0.1068\) & \(0.0657\) & \(0\) & \(-0.3947\) & \(0.0000\) & \(1\) & \(0.2880\) \\ \hline \multirow{2}{*}{\(11\)} & Zero-Lag Cross-Correlation & \multirow{2}{*}{\(0.0720\)} & \multirow{2}{*}{\(0.2152\)} & \multirow{2}{*}{\(0\)} & \multirow{2}{*}{\(-0.2848\)} & \multirow{2}{*}{\(0.0000\)} & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(0.2128\)} \\  & Coeff. (\(\rho_{\text{3y}}\)) & & & & & & & \\ \hline
5 & Kurtosis (\(K\)) & \(-0.1509\) & \(0.0091\) & \(1\) & \(-0.2610\) & \(0.0000\) & \(1\) & \(0.1101\) \\ \hline \multicolumn{3}{c}{**Average**} & \multicolumn{2}{c}{\(0.1099\)} & \multicolumn{4}{c}{\(0.3135\)} & \multicolumn{4}{c}{\(0.2036\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Information-theoretic features ranked by correlation coefficient. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Feature Name**} & \multicolumn{4}{c}{**Before Normalization**} & \multicolumn{4}{c}{**After Normalization**} & \multirow{2}{*}{**Coef Diff**} \\ \cline{3-3} \cline{5-8}  & & **Coef** & \(p\)**-Value** & \
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef** & \(p\)**-Value** &
\begin{tabular}{c} **Predictable** \\ **(\(p\) \textless{} 0.05)** \\ \end{tabular} & **Coef Diff** \\ \hline
1 &
\begin{tabular}{c} AvgPower \\ WEB \\ EB (0.5–3 Hz) \\ PeakFreq. \\ SNRF \\ SNRF \\ \end{tabular} & \(-0.1345\) & \(0.0202\) & \(1\) & \(-0.3990\) & \(0.0000\) & \(1\) & \(0.2645\) \\
2 & \(-0.1393\) & \(0.0161\) & \(1\) & \(-0.3974\) & \(0.0000\) & \(1\) & \(0.2581\) \\
3 & \(-0.1409\) & \(0.0149\) & \(1\) & \(-0.3347\) & \(0.0000\) & \(1\) & \(0.1937\) \\
4 & \(-0.1239\) & \(0.0325\) & \(1\) & \(-0.3196\) & \(0.0000\) & \(1\) & \(0.1958\) \\
5 & \(-0.2669\) & \(0.0000\) & \(1\) & \(-0.2471\) & \(0.0000\) & \(1\) & \(-0.0199\) \\
6 & \(-0.1385\) & \(0.0168\) & \(1\) & \(-0.1734\) & \(0.0027\) & \(1\) & \(0.0349\) \\
7 & \(-0.0925\) & \(0.1111\) & \(0\) & \(-0.1703\) & \(0.0032\) & \(1\) & \(0.0778\) \\
8 & \(-0.1179\) & \(0.0420\) & \(1\) & \(-0.1525\) & \(0.0084\) & \(1\) & \(0.0346\) \\ \hline \multicolumn{3}{c}{**Average Useful**} & \multicolumn{2}{c}{\(0.1443\)} & \multicolumn{4}{c}{\(0.2742\)} & \multirow{2}{*}{\(0.1299\)} \\ \hline
9 & Bandwidth (B) & \(-0.0682\) & \(0.2408\) & \(0\) & \(-0.0795\) & \(0.1711\) & \(0\) & \(0.0000\) \\
10 & \(-0.0910\) & \(0.1168\) & \(0\) & \(0.0393\) & \(0.4996\) & \(0\) & \(0.0000\) \\
11 & \(-0.1056\) & \(0.0687\) & \(0\) & \(0.0362\) & \(0.5334\) & \(0\) & \(0.0000\) \\ \hline \multicolumn{3}{c}{**Average All**} & \multicolumn{2}{c}{\(0.1314\)} & \multicolumn{4}{c}{\(0.2313\)} & \multicolumn{4}{c}{\(0.0999\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Frequency-domain features ranked by correlation coefficient.