Prediction of three-dimensional crutch walking patterns using a torque-driven model
Por:
Febrer-Nafria, Miriam, Pallares-Lopez, Roger, Fregly, Benjamin J. and Font-Llagunes JM
Publicada:
1 ene 2021
Resumen:
Computational prediction of 3D crutch-assisted walking patterns is a
challenging problem that could be applied to study different
biomechanical aspects of crutch walking in virtual subjects, to assist
physiotherapists to choose the optimal crutch walking pattern for a
specific subject, and to help in the design and control of exoskeletons,
when crutches are needed for balance. The aim of this work is to
generate a method to predict three-dimensional crutch-assisted walking
motions following different patterns without tracking any experimental
data. To reach this goal, we collected gait data from a healthy subject
performing a four-point non-alternating crutch walking pattern, and
developed a 3D torque-driven full-body model of the subject including
the crutches and foot- and crutch-ground contact models. First, we
developed a predictive (i.e., no tracking of experimental data) optimal
control problem formulation to predict crutch walking cycles following
the same pattern as the experimental data collected, using different
cost functions. To reduce errors with respect to reference data, a cost
function combining minimization terms of angular momentum, mechanical
power, joint jerk and torque change was chosen. Then, the problem
formulation was adapted to handle different foot- and crutch-ground
conditions to make it capable of predicting three new crutch walking
patterns, one of them at different speeds. A key aspect of our algorithm
is that having ground reactions as additional controls allows one to
define phases inside the cycle without the need of formulating a
multiple-phase problem, thus facilitating the definition of different
crutch walking patterns.
Open Access
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