Application of a functional method for subject and motion specific joints kinematics during walking

Quantitative, as well objective human motion analysis has a prominent role in many research fields of biomechanics, such as for gait analysis purposes. Focusing on joints kinematics, several aspects like the kind of movement, the measurements, the post-processing techniques and the adopted human multi body model within the joints approximation have to be taken into account to ensure a consistency of outcomes both in terms of reliability and repeatability. In particular, both kinematic and dynamic calculations require an accurate method of joints parameters estimation, like centers and axes of rotation, resulting in a different definition of joints coordinates systems and of motion patterns computation, as a consequence. Predictive and functional methods can be used to estimate joints parameters. Considering some drawbacks like the restrictive protocol for markers positioning on precise anatomical landmarks and the following lack of accuracy, which are typical of the former approach, the aim of this study is to evaluate the feasibility of a functional method to simultaneously estimate lower limbs joints parameters during walking. Markers-based stereo photogrammetric motion capture sessions are performed in order to acquire data to be used as input of a functional algorithm. A validation of this latter is provided with a subject-specific multi body model, implemented in OpenSim, properly scaled by means of the estimated joint centers. Moreover, gait kinematics curves are computed by solving an inverse kinematics problem exploiting a global optimization method. A final comparison with the gold standard technique is provided in terms of root mean squared errors and cross-correlation coefficients to evaluate the outcomes consistency for the presented pilot stud