## 4 Discussion
Analyzing the sagittal plane kinematics of athletes in the field can provide sports science and medical practitioners with useful insights into their athletes' physical condition and running performance. Trunk flexion/extension angle has been shown to increase when localized muscular fatigue is present [39; 40]. Additionally, more experienced runners typically have less trunk flexion and reduced peak hip flexion during foot contact, resulting in increased performance [41] and reduced injury risk [42]. Segment velocities can also be used to characterize performance, as thigh flexion/extension angular velocity has been shown tobe a determining factor during sprint running [43]. Our results showed that all of these variables could be accurately estimated with ANNs, thus providing practitioners with a method to quantify running performance and monitor fatigue in the field. The findings of this study can also be used in relation to sports injury analysis. Accurate estimations of knee flexion/extension angle were found, which could be employed in connection with anterior cruciate ligament (ACL) injuries. Athletes who have undergone ACL reconstruction can limit knee extension during running for up to one-year post-surgery [44]. Therefore, monitoring this variable during rehabilitation can provide insights into an athlete's progress. It has also been suggested that athletes with limited knee flexion during running, jumping, and cutting tasks have a greater risk of suffering noncontact ACL injuries [45], which is prevalent among female athletes [46]. However, it must be stated that the current study analyzed only the prediction capabilities during straight-line, steady-state running. Whether the same accuracies in running kinematic estimation can be achieved in jumping and multidirectional tasks remains unclear. However, the present study offers the potential for practitioners to accurately measure sagittal plane kinematics that are of interest in hip and knee injuries. Furthermore, the methodology employed in this study utilized commercially available software (IBM SPSS Modeler software version-22) to compute the MLP ANNs. Using this software does not require knowledge or experience in building and training ANNs. Most sports science and medicine university degrees teach the use of SPSS software during their research methods modules. Thus, the methodology used in the present study is reproducible by those sports science and medical practitioners who have undergone a university degree and can be introduced into their daily practices. The limitations of the present study are that the output variables were specific to gait events. Analyzing joint/segment angles and velocities over the whole gait cycle would provide a more comprehensive insight into the capacity of ANNs to predict running kinematics with data from GPS devices and anthropometric measurements. Additionally, MLPs are a simple form of ANN, and greater accuracy may be achieved with more complex classes of ANNs [3]. Moreover, the present study used a small number of participants. Considering the high accuracies reported here, increasing the sample size would allow for more data to train the ANN model and lead to potentially stronger predictions. Future research could explore this concept and aim to estimate kinematics over the whole gait cycle instead of specific time events. In addition, including multidirectional running would allow for kinematic analysis of the more sports-specific movement tasks that athletes complete in the field.