# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 3 Results
### Binary Classification
#### 3.1.2 Hard-Soft Classification  
The hard-soft terrains classification is a binary classification case where the surfaces were categorized into hard surfaces (concrete floor, asphalt, and tiles) and soft surfaces (carpet, grass, and soil). The best classification accuracy of 92.08% (\(\pm\)0.25) was observed with RF for the lower back sensor when trained on all features. On the same features set and the same lower back sensor, the SVM produced an average classification accuracy of 90.64% (\(\pm\)0.33). For the chest, the RF achieved classifications accuracy of 89.08% (\(\pm\)0.28), whereas the SVM achieved classification accuracy of 89.57% (\(\pm\)0.31). Figure 5 shows the confusion matrices computed by RF and SVM on all features using the lower back sensor. Detailed classification results for different sensor placements against different features sets are presented in Table 6. A trend similar to hard-soft classification was observed again where the models trained with _all features_ produced the highest classification accuracies for all sensor placements.  
Figure 5: Confusion matrices of _hard–soft terrain classification_ computed with lower back sensor. Each column represents a feature set: (**a**) all features (194 features), (**b**) time (110 features), (**c**) frequency (84 features), (**d**) accelerations (97 features) and (**e**) angular velocities (97 features). Each row represents a classifier (from (**top**) to (**bottom**)): RF and SVM. Classes: \(C_{T}^{S}=soft\) (carpet, grass, and soil) and \(C_{T}^{H}=hard\) (concrete, Asphal, and tiles).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Classification Category**} & \multirow{2}{*}{**Sensor**} & \multicolumn{10}{c}{**Accuracy (\%)**} & \multicolumn{10}{c}{**Statistical Analysis**} \\ \cline{3-14}  & & \multicolumn{3}{c}{**All Features**} & \multicolumn{3}{c}{**Temporal Features**} & \multicolumn{3}{c}{**Spectral Features**} & \multicolumn{3}{c}{**3D Accelerations**} & \multicolumn{3}{c}{**3D Angular Velocities**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multicolumn{3}{c}{**\#1-Score (\%)**} \\ \cline{3-14}  & & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF \\ \hline \multirow{3}{*}{**Indoor-Outdoor**} & Lower Back & **97.48** & **96.52** & **96.73** & **96.93** & **94.47** & **93.58** & 95.80 & 93.83 & 92.25 & 93.33 & 97.2 & 96.6 & 97.2 & 96.6 & 97.1 & 96.6 \\ \cline{2-14}  & Chest & 97.11 & 95.72 & 95.88 & 95.89 & 93.83 & 91.32 & **96.11** & **96.66** & 89.00 & 88.61 & 97.5 & 95.5 & 97.5 & 95.5 & 97.5 & 95.5 \\ \hline \multirow{3}{*}{**Hard-Soft**} & Lower Back & **90.64** & **92.08** & **86.69** & **91.81** & **85.19** & **87.16** & 85.19 & 88.46 & 79.87 & 86.39 & 89.1 & 91.8 & 89.0 & 91.7 & 89.1 & 91.7 \\ \cline{2-14}  & Chest & 89.57 & 89.08 & 84.41 & 89.38 & 83.76 & 82.34 & **85.98** & **88.50** & 78.07 & 77.45 & 89.4 & 87.7 & 89.4 & 87.6 & 89.3 & 87.6 \\ \cline{2-14}  & Lower Back & **96.53** & **96.00** & **95.05** & **96.