Motion analysis forms the cornerstone in multiple application domains, ranging from clinical diagnostics and biomechanics to the animation industry. To acquire kinematic information, optical motion capture is the gold standard. Here, the 3D motion of the unobservable skeleton is inferred from tracking the position of the reflective markers applied to the skin. Yet, the skin is not rigidly attached to the skeleton, introducing relative skin movements. In computer graphics, this skin movement is considered to be essential to create realistic looking avatars. However, in rehabilitation sciences, the skin motion interferes with the accurate extraction of joint kinematics and kinetics and is therefore considered as noise. In this PhD fellowship, a novel methodology to determine more accurate lower limb kinematics will be developed. The skin shift will not be treated as noise but will be seen as an important source of information. Herefore, methods from clinical biomechanics and computer graphics will be blended. The skin geometry deformation will be both measured and modelled. By enforcing consistency between the measured and predicted skin surface, the kinematics will be determined.