Biomedical Engineering Reference
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Nevertheless, the dynamic model shows some difficulties predicting the shoulder
angle accurately. This may be due in part to the absence of the necessary con-
straints on the complex shoulder motion in the model and in part to the difficul-
ties associated with computing accurate shoulder joint motion from the
experimental data. The latter could be contributed to the complex structure of the
shoulder joint and the high degree of coupling with the adjacent joints in the
human body during the lifting motion. It is noted that the validation for the kinet-
ics data is ongoing and will be reported later.
8.9
Concluding remarks
This chapter has presented an implementation of predictive dynamics as a lifting
task. The method is broadly applicable to lifting a load from one location to
another. The motion planning was formulated as a large-scale nonlinear program-
ming problem. Joint profiles were discretized using cubic B-splines, and the cor-
responding control points were treated as unknowns for the optimization problem.
Two objective functions were used in the lifting formulation: dynamic effort and
stability as a weighted sum approach in a multi-objective optimization problem.
Based on the simulation data, the ability of the proposed methodology to select a
realistic human lifting strategy was demonstrated.
References
Arisumi, H., Chardonnet, J.R., Kheddar, A., Yokoi, K., 2007. Dynamic lifting motion of
humanoid robots. 2007 IEEE International Conference on Robotics and Automation,
Roma, Italy, pp. 2661
2667.
Hariri, M., 2012. A Study of Optimization-Based Predictive Dynamics method for Digital
Human Modeling, PhD Dissertation. The University of Iowa, Iowa City, IA.
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