## 1 Introduction
Several classes of ANNs have been employed in studies that have attempted to predict biomechanical parameters during locomotion. Among these classes, multilayer perceptron (MLP) networks have performed well when predicting joint kinematics during walking [1; 3; 5]. MLP networks are considered a simpler form of ANN; they are easy to train and often employed as the baseline ANN to compare newer models against [3]. MLPs have shown an rRMSE of \(<\)9% for lower limb kinematics during walking [5]. However, this error has increased to \(\sim\)34% when analyzing knee moments during running-based tasks [6]. As a result, more complex ANNs (convolutional neural networks) have been developed, integrating musculoskeletal simulations into the training module to improve accuracy [22; 23]. However, these models' pre-calibration procedures are extensive and require access to a biomechanics lab. In principle, however, the observed increased accuracy highlights the value of utilizing additional biomechanical input variables in ANNs to predict joint kinematics. Previous investigations utilizing IMUs and ANNs to predict joint kinematics have typically stemmed from a clinical background [1; 4]. Their application within sports has yet to be explored. With team sports athletes, IMUs within global positioning systems (GPS) are commonly used to monitor athletes' workloads along with GPS-derived variables such as distance and running speed [24; 25]. The GPS devices are mounted at approximately the posterior aspect of the upper thoracic spine, and the IMUs capture the acceleration profile of the upper trunk segment. There is the potential to use a single GPS-based IMU to measure the running kinematics of a player in the field. Previous research from the author found that the acceleration peaks captured by the GPS-based IMU are significantly influenced by an individual's running kinematics [26]. More specifically, the peak velocities of the segments during foot contact had, on average, a greater effect on the acceleration peaks than joint/segment angles [26]. However, the accuracy of employing GPS-based IMU data to directly estimate the kinematic profiles of joints and segments remains unclear. Athlete anthropometrics [27], stance time [28], and running speed [29; 30] have also been shown to have a relationship with an athlete's running kinematics. These variables are accessible to practitioners working with team sports athletes. Anthropometric measurements are routinely collected during skinfold body composition assessments [31]. Running speed can be accurately measured with coordinate data derived from GPS devices [32]. Stance time can also be measured with GPS-based IMU data [24; 26]. Considering these, it was hypothesized that including these variables in addition to the IMU data could potentially increase the accuracy of ANNs in predicting running kinematics. Including accurate estimations of an individual's running kinematics using GPS-based IMUs can offer more detailed biomechanical analyses of their performance in the field. The capacity to utilize IMUs within GPS devices to conduct field-based biomechanical analysis offers a tool for tracking an athlete's progression during injury rehabilitation. Therefore, the present study aims to investigate whether running kinematics can be accurately estimated through an ANN model containing GPS-based accelerometer variables, running speed, and anthropometric data. This study intends to explore the predictive capabilities of data easily accessible to sports science and medical practitioners to conduct comprehensive biomechanical analyses in the field.