## 4 Discussion  
Based on the results presented in the previous section, several key findings are presented.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Classifier** & **Acc.** & **F1 Score Score** & **AUC Score** & **TP Rate** & **FP Rate** & **Prec.** & **Method** & **Device** & **Features** \\ \hline [PERSON] et al. [82] & 70\% & 0.786 & 0.825 & — & — & — & — & J48 & Phone, Watch & Skew, Kurtosis, Gait Velocity, Residual Step Time, Band Power, XZ Sway, XY Sway, YZ Sway Volume \\ \hline [PERSON] et al. [81] & 62\% & — & — & — & — & — & — & Conv. Neural Network & Phone, Watch & Raw data \\ \hline  & & & & & & & & & Number of Steps, Average Step Time, Average Cadence, Skewness, Kurtosis, Coefficient of Variation of Step Time, Harmonic Ratio, Average Step Length, Gait Velocity, Minimum and Maximum Difference, Standard Deviation, Root Mean Square, Entropy Rate, Regression Line for Local Maxima and Minima, Average Power, Ratio of Spectral Peak, Signal-to-Noise Ratio, Total Harmonic Distortion, Energy in Band 0.5 to 3 Hz, Powlosed Energy in Band 0.5 to 3 Hz, Peak Frequency, Spectral Centroid, Bandwidth, Regression Line for Windowed Energy, Wavelet Bandwidth, Wavelet Entropy Rate, Zeroth-Lag Cross-Correlation Coefficient, Lampel-Ziv Complexity \\ \hline \hline \end{tabular} Note: Acc. = accuracy, Prec. = precision, TP Rate = true positive rate, FP Rate = false positive rate. \"Phone\" and “Watch” refer to smartphone and smartwatch devices, respectively. The table summarizes classification performance and features used in prior and current studies for intoxication detection based on gait data.  
\end{table}
Table 31: Comparison of performance metrics with prior work.  
First, the analysis of the boxplot and predictability report indicates that normalization significantly enhances the performance of most features. Specifically, 20 out of 22 features with \(p\)-values \(<\) 0.05 showed improvement after normalization. This finding supports existing research that emphasizes the role of data normalization or standardization in improving machine learning performance on sensor data [83]. Particularly, preprocessing was found to significantly boost the performance of random forest classifiers on human activity recognition gait data, a finding consistent with our results.  
Second, statistical-domain features exhibited the highest predictive accuracy of 83.89%. This was followed by time-domain features with an accuracy of 83.22%, and frequency-domain features, which achieved an accuracy of 82.21%. These results highlight the importance of statistical properties in distinguishing patterns within the data. Above all, our findings align with previous findings that statistical features such as kurtosis provide rich descriptors of motion patterns [84]. This work aligns with our findings that time and frequency domain features are top performers as well, after statistical features. It is worthy of note that some features can be categorized as both statistical and time-domain features. Our results strengthen the argument for prioritizing statistical features in intoxication detection.  
Third, the frequency-domain features may benefit from the application of time-frequency transform techniques. Preliminary experiments suggest that \(p\)-values vary depending on the choice of spectral estimation method, such as Welch, FFT, and DCT, when computing the ratio of spectral peaks. This highlights the potential for further optimization using advanced spectral analysis techniques.  
Fourth, most extracted features demonstrate strong predictive capabilities. Out of the 27 tested features, 22 features have shown strong predictive capabilities. The results suggest that refining and optimizing feature extraction methods could further enhance classification accuracy.