## 4 Discussion  
This study aimed to determine the predictive capabilities of ANNs containing GPS-based accelerometer, running speed, and anthropometric data to estimate running kinematics. The study's results provide preliminary insights into the application of deep learning algorithms with data that are easily accessible to sports science and medical practitioners working with team sports athletes to conduct analyses of running kinematics in the field. Correlation coefficients of the estimated vs. actual output variables ranged from strong to excellent across all output variables, showing that the model's performance was highly accurate. Overall, estimations of joint/segment angles (mean rRMSE = 13.0 \(\pm\) 4.3%) were better than peak segment velocities (mean rRMSE = 22.1 \(\pm\) 14.7%). Previous research [26] found that peak segment velocities had a greater effect on GPS-based accelerometer peak accelerations than joint/segment angles in linear regression-based analysis. Conversely, in the current study, better accuracy was observed in predicting joint/segment angles than in predicting peak segment velocities. This indicates a stronger relationship between the input variables and joint/segment angles. Including added input variables (running speed, stance times, anthropometric) or the capability of ANNs to utilize non-linear relationships may be responsible for this finding. Sensitivity analysis of the input variables highlighted that the GPS-based IMU variables were, on average, not the most important variables in both joint/segment and peak segment velocities estimations (Figures 2 and 3). However, the performance of each ANN must be considered. Accelerometer peak acceleration had the highest relative importance in estimating thorax flexion/extension at MS, which was the most accurate estimation of all joint/segment angles. Despite this, the value of including anthropometric input variables in ANNs has become evident in the current study. The input variables with the highest mean relative importance for joint/segment angles were participant height (75 \(\pm\) 25%) and mass (62 \(\pm\) 26%). For peak segment velocities, leg length (right = 63 \(\pm\) 29%; left = 60 \(\pm\) 27%) and height (60 \(\pm\) 29%) were the highest. Anthropometric data has been previously employed within the pre-calibration procedures of conventional neural networks to improve the accuracy of estimating joint kinematics [3]. Our results also show that anthropometric data can aid multilayer perceptron ANNs instead of relying on IMU data alone. There were five joint angles (Hip flexion/extension at IFC; Thorax flexion/extension at IFC; Thorax flexion/extension at IFC; Pelvis flexion/extension at MS) and two peak segment velocities (Thigh flexion/extension during propulsion; Thigh vertical during propulsion) that had rRMSEs of \(<\)10% (Figure 1). Previous investigations have typically only analyzed lower limb kinematics and found hip flexion/extension RMSE values of 5.1-5.6\({}^{\circ}\) and knee flexion/extension RMSE values of 4.8-6.5\({}^{\circ}\) during running [22; 23]. The present study differed from previous investigations, as estimations of joint/segment angles at specific gait events were estimated rather than the continuous joint angle over the whole gait cycle and the type of ANN used. Nevertheless, our results showed that the RMSE values of knee flexion/extension at IFC were 2.47\({}^{\circ}\). It can be suggested that MLP ANNs could achieve similar or greater accuracy in estimating hip and knee sagittal joint angles than previous methods. These findings also highlight the importance of including anthropometric data, as they had the highest importance in four of the five most accurate variables (Figure 1). Accurate estimations of peak thigh flexion/extension and vertical velocities during propulsion were also observed (rRMSE \(\leq\) 9.3%). However, there are no previous studies against which to compare these results. There are several practical applications of the present study's findings.