In the literature, conventional 3D inverse dynamic models are limited in three aspects related to inverse dynamic notation, body segment parameters and kinematic formalism. First, conventional notation yields separate computations of the forces and moments with successive coordinate system transformations. Secondly, the way conventional body segment parameters are defined is based on the assumption that the inertia tensor is principal and the centre of mass is located between the proximal and distal ends. Thirdly, the conventional kinematic formalism uses Euler or Cardanic angles that are sequence-dependent and suffer from singularities. In order to overcome these limitations, this paper presents a new generic method for inverse dynamics. This generic method is based on wrench notation for inverse dynamics, a general definition of body segment parameters and quaternion algebra for the kinematic formalism.
In this paper, a semi-automatic method for segmenting pressure distribution image-based data at the body-seat interface is presented. The purpose of this work was to estimate the surface and the load supported by the ischial tuberosity (IT) region. The proposed method involves three steps: (1) detecting the IT region using a pressure-distribution image gradient; (2) estimating the contour of the IT region by an iterative active contour algorithm and finally (3) estimating the percentage of the surface and the weight-bearing of the IT region in a group of able-bodied (AB) and spinal-cord injury (SCI) subjects. It was found in this study that the weight bearing on the IT for the spinal-cord injured group is distributed on half the surface in comparison with the AB group or the powered wheelchair users groups. The findings of this study provide insights concerning pressure distribution in sitting for the paraplegic and able-bodied.
This study evaluated the association of contact point locations with the knee medial and lateral contact force (Fmed, Flat) alterations in OA and healthy subjects. A musculoskeletal model of the lower limb with subject-specific tibiofemoral contact point trajectories was used to estimate the Fmed and Flat in ten healthy and twelve OA subjects during treadmill gait. Regression analyses were performed to evaluate the correlation of the contact point locations, knee adduction moment (KAM), knee flexion moment (KFM), frontal plane alignment, and gait speed with the Fmed and Flat. Medial contact point locations in the medial–lateral direction showed a poor correlation with the Fmed in OA (R2 = 0.13, p = 0.01) and healthy (R2 = 0.24, p = 0.001) subjects. Anterior–posterior location of the contact points also showed a poor correlation with the Fmed of OA subjects (R2 = 0.32, p < 0.001). Across all subjects, KAM and KFM remained the best predictors of the Fmed and Flat, respectively (R2 between 0.62 and 0.69). Results suggest different mechanisms of contact force distribution in OA joints. The variations in the location of the contact points participate partially to explains the Fmed variations in OA subjects together with the KFM and KAM.