@inproceedings {936,
	title = {Simulating Human Upper and Lower Limb Balance Recovery Responses Using Nonlinear Model Predictive Control},
	booktitle = {Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
	year = {2021},
	month = {12/2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Mexico},
	abstract = {<p>The ability to generate predictive dynamic simulations of human movement using optimal control has been a growing point of interest in the design of medical/assistive devices, e.g. robotic exoskeletons. Despite this, many disseminated simulations of whole-body tasks, such as balance recovery, neglect the role of the upper body instead focusing on postural joints, e.g. ankle, knees, hips. Thus, the purpose of the current study was to use a novel nonlinear model predictive control (NMPC) approach to assess how actuated upper limbs, as well as different individual performance (optimality) criteria, can shape simulated reactive balance recovery responses. A sagittal biomechanical model of a young adult standing was designed and actuated via nonlinear muscle torque generators (rotational single-muscle equivalents). Forward dynamic simulations of balance recovery (NMPC-driven) following an unexpected support-surface perturbation were generated for each unique combination of selected performance criteria (6 total), perturbation direction (forward and backward), and arm joints free/locked. The observed joint trajectories provide insight into the emergence of human elements of postural control from individual optimality criteria, e.g. hip-ankle strategies emerge from single-joint regulation. Quantitative analysis of performance improvements with the arms free suggest that whether arm responses emerge in the simulations may be dependent on the problem\&rsquo;s initial guess. Future work should focus on testing further performance criteria and improving NMPC as a model of the nervous system.</p>
},
	keywords = {Arms, Biological system modeling, Biomechanics, Optimal Control, Perturbation methods, Predictive models},
	doi = {10.1109/EMBC46164.2021.9630208},
	url = {https://ieeexplore.ieee.org/abstract/document/9630208},
	author = {Inkol, Keaton A. and McPhee, John}
}
