Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data.

Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.

Depth camera based statistical shape fitting approach for the creation of an individualized lower body biomechanical model: validity and reliability.

Systems for instrumented motion analysis depend on an accurate biomechanical model. In this work an existing statistical shape fitting approach was adapted to register a parametrized human surface model with annotated anatomical landmarks to a point cloud obtained from one frontal depth camera view. Based on the obtained landmark positions joint centers, segment lengths and segment orientations of the lower body were calculated. The outcome was validated among two groups of healthy and impaired subjects, using a marker-based optical motion capture system. The results reveal a valid and reliable approach for obtaining an individualized lower body biomechanical model with minimum effort.