Table 2.

Pre-evaluation of different shallow learning models on varying window sizes used for data segmentation. The best configuration (classifier and window size) is underlined.

Classifier	Win Size	0.5 s	1 s	2 s
K-Nearest Neighbors		0.890	0.892	0.889
Decision Tree		0.868	0.882	0.879
Support Vector Machine		0.924	0.929	0.930
Naive Bayes		0.861	0.867	0.870
Bagging		0.903	0.910	0.910
Random Forest		0.911	0.918	0.917
ExtraTrees		0.906	0.911	0.911
Gradient Boosting		0.921	0.925	0.925