A simple controller to overcome the lack of correlation between forward and inverse dynamic analysis of human motion tasks

Abstract

The majority of biomechanical analyses of human motions, including those with musculoskeletal models, use inverse dynamic approaches due to its ability to deal with experimentally acquired kinematic and kinetic data. Yet, a forward dynamic approach can be more powerful and provide better insights on the transmission of forces in the internal biomechanical systems and structures of the human body. Although both approaches may use the same biomechanical model the results achieved do not necessarily correlate with each other. The aim of this study is to demonstrate the source of the lack of correlation between inverse and forward dynamics methodologies providing, in the process, insights on how to overcome such differences. Two types of problems involving the biomechanics of the spatial human motion are used to evaluate the correlation between the forward and inverse dynamic approaches: a gait analysis of a deterministic biomechanical model of the lower limbs, and, a full musculoskeletal model of the upper limb, which is characterised by the solution of a redundant muscle force sharing problem. For that purpose, an inverse dynamic model is applied to estimate the forces responsible for two experimentally acquired motions that are, afterwards, given as input to the forward dynamics model, which is used, in turn, to compute the kinematics of the biomechanical model. The comparison between the reference kinematics, acquired experimentally, and that resulting from the forward dynamic analysis supports that a lack of correlation between the inverse and forward dynamic analysis is always observed. It is proposed here, and demonstrated, that a controller implemented in a feedback loop is able to enhance numerical stability of the forward dynamics solution, leading to the ability of the forward dynamics approach to successfully simulate the acquired motions.