Predict Knee Kinematics During Stationary Cycling via Machine Learning Regression Models

Abstract

To improve athletes' performance and prevent injuries, an understanding of the kinematics of lower limbs is becoming increasingly important in rehabilitating lower extremities with cycling ergometer, particularly the knees. Multiple methods are being used for capturing motion and simulating kinematics of motion. Motion capture system is a common method for motion studies, which includes placement of reflective markers on the lower limbs (at about 36 locations), as well as the use of cameras to track the trajectory of markers. However, these methods require complex and expensive equipment, are limited to the laboratory environment, might face some difficulties in finding some trajectories, and are typically expensive and time-consuming. The purpose of this study is to integrate Machine Learning methods with data from a motion capture system to develop a model that can predict where markers will be placed on the knee on the basis of an individual’s anthropometric information and the cycling device dimensions. There are several advantages to this method, including the possibility of expanding it to studies of other parts of the body, and saving time and costs in comparison to capture motion systems. Improving and optimizing the proposed method will pave the road for developing efficient and cost-effective methods for conducting kinematics analyses.