Two-Dimensional versus Three-Dimensional Symmetric Lifting Motion Prediction Models: A Case Study

Symmetric lifting is a common manual material handling strategy in daily life and is the main cause of low back pain. In the literature, symmetric lifting is mainly simulated by using two-dimensional (2D) models because of their simplicity and low computational cost. In practice, however, symmetric lifting can generate asymmetric kinetics especially when the lifting weight is heavy and symmetric lifting based on 2D models miss this important asymmetric kinetics information. Therefore, three-dimension (3D) models are necessary for symmetric lifting simulation to capture asymmetric kinetics. The purpose of this single subject case study is to compare the optimization formulations and simulation results for symmetric lifting by using 2D and 3D human models and to identify their pros and cons. In this case study, a 10 degrees of freedom (DOFs) 2D skeletal model and a 40 DOFs 3D skeletal model are employed to predict the symmetric maximum weight lifting motion, respectively. The lifting problem is formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse dynamic optimization approach is used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. Lab experiments are carried out to validate the predicted motions. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight from the 2D and 3D human models for Subject #8 are compared with the experimental data. Recommendations are given.

An Enhanced Planar Linked Segment Model for Predicting Lumbar Spine Loads during Symmetric Lifting Tasks

The present technical note aimed at enriching the planar linked segment model originally proposed by Chaffin with the prediction of the moment arm and of the orientation of the line of action of the back extensor muscles during symmetric lifting tasks. The prediction equations proposed by van Dieen and de Looze for their single equivalent muscle model were used for such a purpose. Their prediction was based on the thorax-to-pelvis flexion angle as computed from 3D video-based motion capture. In order to make these prediction equations compliant with a two-dimensional analysis, the planar angle formed by the segment joining L5/S1 to the shoulder with the longitudinal axis of the pelvis was introduced. This newly computed planar trunk flexion angle was used to feed van Dieen and de Looze’s equations, comparing the results with the original model. A full-body Plug-in-Gait model relative to 10 subjects performing manual lifting activities using a stoop and a squat technique was used for model validation. A strong association was found between the proposed planar trunk flexion angle and that used by van Dieen and de Looze (r = 0.970). A strong association and a high level of agreement were found between the back extensor muscle moment arm (r = 0.965; bias < 0.001 m; upper limit of agreement (LOA) = 0.002 m; lower LOA < 0.001 m) and the orientation of the line of action (r = 0.970; bias = 2.8°; upper LOA = 5.3°; lower LOA = 0.2°) as computed using the two methods. For both the considered variables, the prediction error fell within the model sensitivity.