Research paper entitled “Knee Flexion/Extension Angle Measurement for Gait Analysis using Machine Learning Solution “MediaPipe Pose” and its Comparison with Kinovea®” gets published in IOP Conference Series: Materials Science and Engineering

Abstract
Human Gait Analysis is crucial in diagnosis, monitoring, treatment, and rehabilitation. It requires the measurement of gait parameters like joint angles, angular motion, ground reaction force (GRF), momentum, step width, step length, velocity, etc. This study introduces a markerless, easy-to-use approach for measuring one of the gait parameters i.e., knee flexion/extension angle, and verifies it with an existing standard marker-based approach. Knee flexion/extension angle is calculated via Machine Learning (ML) pose estimation model “MediaPipe Pose” and Computer vision (CV) without the use of markers. For the verification of accuracy, the obtained values are compared with the data on each video frame obtained from Kinovea® which is a marker-based motion analysis software. A correlation of 0.941 was observed between the results from Kinovea® and MediaPipe Pose. Similarly, the mean absolute error of knee angles was 5.88 degrees. The research shows that knee flexion/extension angle can be accurately measured using ML solution for high-fidelity body pose tracking. Professionals involved in the field of biomechanics, sports medicine, physiotherapy as well as other medical fields can use this method as an alternative markerless approach for knee flexion/extension angle measurement for gait analysis.