###### Abstract  
Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (\(F_{x}\), \(F_{y}\), \(F_{z}\)), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding \(F_{z}\) it reached 0.92.  
biomechanics; artificial intelligence; deep learning; force prediction; treadmill; dataset; recurrent neural network; LSTM +
Footnote †: journal: Medicalfor injury assessment in sports, there is a gap in the development of real-time applications in this field [14]. The aim and scope of this study was to demonstrate the results of a proposed framework using a database of physical and biomechanical markers of joints during treadmill exercises. The use of video for biomechanics research is not new; there have been various studies analyzing biomechanical parameters from videos of athletes. Two sub-fields exist when discussing video-based biomechanical tools: marker and markerless analysis. Marker-based-tools rely on placing markers onto the athlete's joints to track them in the video (and thencforth process relevant information); markerless research is not constrained by this and instead uses artificial vision strategies to identify the joints in the video [10; 15]. Although more complex (computationally speaking), markerless analysis is better suited for real-life scenarios (e.g., daily activities outside the laboratory). Although some studies reported biomechanical sports research using both methods, the vast majority focused on obtaining joint angles (to assess and correct technique [16], or risk of injuries [4]) and the position/speed of relevant markers [17] and did not analyze the generated forces in a detailed manner. A recurrent neural network (RNN) is a type of artificial neural network (ANN) which is composed of a series of neurons that learn from data and adapt to it by changing their weights, hence being dynamic in the feed-forward process. More specifically, RNNs have the peculiarity that their structure allows them to learn temporal sequences [18]. This, in turn, makes them the gold standard for analyzing biomechanics, kinetics, and kinematics in a three-dimensional motion environment. Recent neuroscience findings have suggested that motor cortices can be modeled as a dynamical system. This perspective is opposite to the traditional view that neural activities in motor cortices represent the kinetic parameters of movements [19]. The adoption of neural networks allows the computation of joint contact force and muscle force via musculoskeletal modeling techniques, which offer valuable metrics for describing movement quality, thereby having numerous clinical applications and research applications. Critical applications include exercise-induced fatigue (considered one of the essential factors leading to musculoskeletal disorders), detection of fall-risk-linked balance impairment, and the ability to measure movement behavior through \"skeletonized\" data and 3D shapes.