## 1 Introduction  
In recent years, there has been an exploration of the use of inertial measurement units (IMUs) to estimate joint kinematics during walking and running [1; 2; 3; 4; 5; 6]. Conducting biomechanical analysis in the field with IMUs allows sports science and medical practitioners to capture an individual's locomotion characteristics [7]. The use of IMUs allows for more frequent analysis and reduces cost/set-up time compared to lab-based analysis [7]. Traditionally, IMUs, such as accelerometers, have been employed to analyze the acceleration profiles of bodily segments [8; 9; 10]. This type of analysis indirectly measures walking/running kinematics to observe differences between individuals or changes under conditions [8; 9; 10]. However, with recent advances in data processing procedures, it is now possible to directly estimate joint kinematics in the field with IMUs [1; 2; 3; 4; 5]. Conventional models utilizing IMUs to estimate joint kinematics have been previously employed to analyze patients' walking kinematics. Analyses of patients rehabilitating from hip arthroplasty [11] and knee ligament reconstruction [12] were used to inform the long-term effects on patients' post-surgery functionality. These earlier models mount IMUs on the proximal/distal segments to the relevant joint and measure the IMU sensor orientation relative to the inertial frame [13]. Lab-based pre-calibration is required to ensure accurate sensor-sensor alignment to the anatomical axes and measurement of segment geometry so that a joint orientation matrix can be calculated [13]. The IMUs are utilized to reconstruct a biomechanical model of the human body. The linear relationships between the sensor acceleration profiles are then used to estimate the joint kinematics [2; 14; 15]. Accuracy is an important aspect of such measurements. Integration of accelerometer, gyroscope, and magnetometer data with sensor fusion algorithms has shown root mean squared errors (RMSEs) of \(<\)3.6\({}^{\circ}\) when estimating 3D lower limb joint angles in the lab [16]. However, concerns about using IMUs to measure segmental orientation have been previously stated when applying these systems in the field [13]. Magnetometers are employed to reduce drift errors in the gyroscope's angular velocity data by \"resetting\" the sensor orientation. Nevertheless, magnetic disturbances in the field can affect the magnetometer's reference coordinates and are thus not recommended for field base use [13; 17]. Additionally, soft tissue motion can lead to misalignment of the IMU sensors [18]. This is a common issue when conducting longitudinal analysis and greatly reduces the accuracy of models that rely on the linear relationship between two aligned sensors [2]. As a result, there is a lack of research that has successfully implemented these methods in the field over longer periods [19]. Recent developments in advanced data processing procedures have overcome previous regression-based problems with sensor misalignment [2], leading to the renewal of the use of IMUs to obtain field-based predictions of joint kinematics [2]. One of the deep learning algorithms is the artificial neural network (ANN). This allows for greater predictive accuracy than linear regression-based models when non-linear relationships between independent (input) and dependent (output) variables exist [20]. ANNs consist of interconnected units (neurons) separated into three layers: input, hidden, and output. Each layer contains several neurons, which are connected and appropriately weighted, depending on the strength of the connection between neurons [20; 21]. Each ANN consists of training and testing modules. In the training module, the model learns the relationship between variables and appropriately \"weights\" each connection, depending on the fitting of the data structure [20]. The testing module then tests the algorithm to analyze how accurately the model has predicted the output variable.