Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

A Review of Kinematic Theories and Practices Compiled for Biomechanics Students and Researchers

The topic of kinematics is fundamental to engineering and has a significant bearing on clinical evaluations of human movement. For those studying biomechanics, this topic is often overlooked in importance. The degree to which kinematic fundamentals are included in Biomedical engineering (BmE) curriculums is not consistent across programs and often foundational understandings are gained only after reading literature if a research or development project requires that knowledge. The purpose of this paper is to present the important theories and methods of kinematic analysis and synthesis that should be in the "toolbox" of students of biomechanics. Each topic is briefly presented accompanied by an example or two. Deeper learning of each topic is left to the reader, with the help of some sample references to begin that journey.

Multi-Output Sequential Deep Learning Model for Athlete Force Prediction on a Treadmill Using 3D Markers

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 (Fx, Fy, Fz), 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 Fz it reached 0.92.