# Skill Level Classification in Basketball Free-Throws Using a Single Inertial Sensor
## 3 Results  
Initially, the accelerometer and gyroscope data collected from the IMU were segmented for each shooting attempt. Figure 3 displays the segmented accelerometer and gyroscope data in the time domain for participants from the two groups, with the solid line representing the mean and the shadow indicating the standard deviation across athletes in each  
Figure 2: Structure of the convolutional neural network. group. We investigated whether any differences between the two skill levels could be detected utilizing traditional statistical methods. Statistical parametric mapping indicated no significant differences in the acceleration and gyroscope data between the groups in the time domain (\(p>0.05\)). Based on these findings, we explored the use of frequency domain information from the IMU data to predict basketball player performance levels. Then, the spectrograms of each player's shooting attempts from both the professional and recreational groups were calculated using FFT. Considering that the spectrograms are two-dimensional variables and could be regarded as an image, a CNN model was utilized to predict the basketball players' levels. Ultimately, we discovered that the CNN could predict the two types of players depending on the direction and type of kinematic datasets. A summary of the classification model's results can be found in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Feature** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline A\({}_{\text{X}}\) & **0.82** & 0.79 & 0.84 \\ A\({}_{\text{Y}}\) & 0.71 & 0.68 & 0.74 \\ A\({}_{\text{Z}}\) & 0.80 & 0.78 & 0.83 \\ \hline G\({}_{\text{X}}\) & 0.73 & 0.69 & 0.75 \\ G\({}_{\text{Y}}\) & 0.77 & 0.73 & 0.79 \\ G\({}_{\text{Z}}\) & 0.74 & 0.72 & 0.76 \\ \hline A\({}_{\text{X}}\)A\({}_{\text{Y}}\) & **0.85** & 0.84 & 0.87 \\ A\({}_{\text{X}}\)A\({}_{\text{Z}}\) & **0.87** & 0.86 & 0.87 \\ A\({}_{\text{Y}}\)A\({}_{\text{Z}}\) & 0.82 & 0.81 & 0.83 \\ \hline G\({}_{\text{Y}}\)G\({}_{\text{Y}}\) & 0.82 & 0.81 & 0.83 \\ G\({}_{\text{X}}\)G\({}_{\text{Z}}\) & 0.80 & 0.77 & 0.82 \\ G\({}_{\text{Y}}\)G\({}_{\text{Z}}\) & 0.81 & 0.78 & 0.83 \\ \hline A\({}_{\text{X}}\)G\({}_{\text{X}}\) & **0.85** & 0.83 & 0.87 \\ A\({}_{\text{Y}}\)G\({}_{\text{Y}}\) & 0.84 & 0.82 & 0.85 \\ A\({}_{\text{Z}}\)G\({}_{\text{Z}}\) & 0.83 & 0.82 & 0.85 \\ \hline A\({}_{\text{X}}\)A\({}_{\text{Y}}\)A\({}_{\text{Z}}\) & 0.87 & 0.86 & 0.88 \\ G\({}_{\text{X}}\)G\({}_{\text{Y}}\)G\({}_{\text{Z}}\) & 0.86 & 0.84 & 0.88 \\ A\({}_{\text{X}}\)A\({}_{\text{Z}}\)G\({}_{\text{Y}}\) & **0.88** & 0.87 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the classification model. Figure 3: Mean (bold line) and standard deviation (shaded) of raw accelerometer and gyroscope data between recreational (blue) and professional basketball players (red). From the result in Table 2, we found that the most accurate individual component was upward acceleration, i.e., \(A_{X}\) (accuracy = 82%; sensitivity = 0.79; specificity = 0.84). The second component with the highest accuracy was forward acceleration, i.e., \(A_{Z}\) (accuracy = 80%; sensitivity = 0.78; specificity = 0.83), and the third was the wrist angular velocity on the sagittal plane, i.e., \(G_{Y}\) (accuracy = 77%; sensitivity = 0.73; specificity = 0.79). Other individual inputs presented lower accuracies, including \(A_{Y}\) (71%), \(G_{X}\) (73%), and \(G_{Z}\) (74%). When two components combined as the input of the CNN model, the best classification accuracy was 0.87 (0.86 sensitivity and 0.87 specificity) for \(A_{X}\) and \(A_{Z}\), higher than the other combinations. When the \(A_{X}\) was with \(A_{Y}\) or \(G_{X}\), the accuracies were both 85%. The accuracy remained similar when all three acceleration axes were conjoined (\(A_{X}\), \(A_{Y}\), \(A_{Z}\)); however, the specificity slightly improved.