# Skill Level Classification in Basketball Free-Throws Using a Single Inertial Sensor
## 4 Discussion  
The overall objective of the current study was to develop an advanced computational model to classify the skill level of basketball players during FT shots using data from a single inertial sensor. We obtained a satisfactory result, with classification accuracy, sensitivity, and specificity greater than 85%. Emerging evidence has shown that machine learning algorithms based on data from wearable sensors can be applied in sports movements classification [17; 18; 19; 20], which may form the basis of biofeedback systems for sports performance enhancement [30]. We utilized a CNN, which has been found to be more effective than other approaches, such as random forest and support vector machines, in accurately classifying sports movements using data from a single inertial sensor [20]. While conventional statistical approaches are unable to classify two groups through sliding window data, the CNN demonstrates satisfactory classification accuracy in analyzing the spectrograms of the IMU data. When the IMU data are segmented and aligned manually with video data, we could observe the preparatory rhythmic lower limb actions  
Figure 4: Training result of the model. of the professional players to establish a comfortable stance or reinforce a rhythm before the shot, while the lower limbs of recreational players remained relatively static. Additional low frequency rippling components of A\({}_{\rm X}\), and A\({}_{\rm Z}\) are observable in professional players, yet were statistically insignificant for classification for two groups in the temporal domain. Such rhythmic extension of lower limbs was also shown to be important to score a shot [31]. These rhythmic lower limb movements are expected to generate energy to increase ball's speed while shooting, as well as facilitate energy transfer from the lower body through the trunk to the upper limb [32]. Our results showed that the classification performance is optimized by considering inputs from A\({}_{\rm X}\), A\({}_{\rm Z}\) and G\({}_{\rm Y}\). Similarly to previous similar studies (e.g., [PERSON] et al. [17]), combining both accelerometer and gyroscope can increase the classification rate of data. A\({}_{\rm X}\) and A\({}_{\rm Z}\) refer to acceleration in upward direction (elevation) and forward translation, respectively. Collectively, these two motions manifest in the trajectory of the ball to formulate the primary ball movement. The results of this study indicate that the speed and concise linear nature with which professional athletes shoot an FT can be distinguish them from recreational players. This is consistent with a previous study [33] which found that highly trained players shoot FTs faster than novice shooters (0.39 \(\pm\) 0.09 s vs. 0.60 \(\pm\) 0.14 s). The study also demonstrated that professional athletes show fewer lateral deviations at the wrist (holding the ball) throughout the entirety of the shot. It can be argued that professional athletes exhibit smoother and more controlled wrist motion during an FT shot. It has been well established that highly skilled athletes show differing kinematics to those of a lower skill, and this study expands upon these differing kinematics [7; 8; 9]. Whilst the inclusivity of G\({}_{\rm Y}\) only increases the accuracy by 1% (88%) (compared to 87% if only consider A\({}_{\rm Z}\) and A\({}_{\rm X}\)), it increases the sensitivity by 1% and specificity by 3%. When adding G\({}_{\rm Y}\) into account, we take into consideration the angular velocity occurring at the wrist joint. This is related to wrist flexion at the end of the shot, known to be prominent in highly skilled athletes as a compensatory technique to maintain shot consistency [34]. This increase in specificity and sensitivity is anticipated to be due to the high importance of the wrist joint in the shot technique of elite shooters [7].