A simple 2D multibody model to better quantify the movement quality of anterior cruciate ligament patients during single leg hop.

Abstract

Patients with anterior cruciate ligament reconstruction frequently present asymmetries in the sagittal plane dynamics when performing single leg jumps but their assessment is inaccessible to health-care professionals as it requires a complex and expensive system. With the development of deep learning methods for human pose detection, kinematics can be quantified based on a video and this study aimed to investigate whether a relatively simple 2D multibody model could predict relevant dynamic biomarkers based on the kinematics using inverse dynamics. Six participants performed ten vertical and forward single leg hops while the kinematics and the ground reaction force "GRF" were captured using an optoelectronic system coupled with a force platform. The participants are modelled by a seven rigid bodies system and the sagittal plane kinematics was used as model input. Model outputs were compared to values measured by the force platform using intraclass correlation coefficients for seven outcomes: the peak vertical and antero-posterior GRFs and the impulses during the propulsion and landing phases and the loading ratio. The model reliability is either good or excellent for all outcomes (0,845 ≤ ICC ≤ 0.987). The study results are promising for deploying the developed model following a kinematics analysis based on a video. This could enable clinicians to assess their patients' jumps more effectively using video recordings made with widely available smartphones, even outside the laboratory.