I got nothing.
I didn't understand these earlier propulsion researches either:
The latest here:
- 1Computational Biomechanics Laboratory,
Department of Mechanical and Aerospace Engineering, University of
Florida, Gainesville, FL, United States
- 2Neuromechanics Laboratory, Wallace H.
Coulter Department of Biomedical Engineering, Emory University and
Georgia Institute of Technology, Atlanta, GA, United States
- 3Motion Analysis Laboratory, Department of
Rehabilitation Medicine, Emory University School of Medicine, Atlanta,
GA, United States
- 4Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
Stroke is a leading cause of long-term disability worldwide and often
impairs walking ability. To improve recovery of walking function
post-stroke, researchers have investigated the use of treatments such as
fast functional electrical stimulation (FastFES). During FastFES
treatments, individuals post-stroke walk on a treadmill at their fastest
comfortable speed while electrical stimulation is delivered to two
muscles of the paretic ankle, ideally to improve paretic leg propulsion
and toe clearance. However, muscle selection and stimulation timing are
currently standardized based on clinical intuition and a
one-size-fits-all approach, which may explain in part why some patients
respond to FastFES training while others do not. This study explores how
personalized neuromusculoskeletal models could potentially be used to
enable individual-specific selection of target muscles and stimulation
timing to address unique functional limitations of individual patients
post-stroke. Treadmill gait data, including EMG, surface marker
positions, and ground reactions, were collected from an individual
post-stroke who was a non-responder to FastFES treatment. The patient's
gait data were used to personalize key aspects of a full-body
neuromusculoskeletal walking model, including lower-body joint
functional axes, lower-body muscle force generating properties,
deformable foot-ground contact properties, and paretic and non-paretic
leg neural control properties. The personalized model was utilized
within a direct collocation optimal control framework to reproduce the
patient's unstimulated treadmill gait data (verification problem) and to
generate three stimulated walking predictions that sought to minimize
inter-limb propulsive force asymmetry (prediction problems). The three
predictions used: (1) Standard muscle selection (gastrocnemius and
tibialis anterior) with standard stimulation timing, (2) Standard muscle
selection with optimized stimulation timing, and (3) Optimized muscle
selection (soleus and semimembranosus) with optimized stimulation
timing. Relative to unstimulated walking, the optimal control problems
predicted a 41% reduction in propulsive force asymmetry for scenario
(1), a 45% reduction for scenario (2), and a 64% reduction for scenario
(3), suggesting that non-standard muscle selection may be superior for
this patient. Despite these predicted improvements, kinematic symmetry
was not noticeably improved for any of the walking predictions. These
results suggest that personalized neuromusculoskeletal models may be
able to predict personalized FastFES training prescriptions that could
improve propulsive force symmetry, though inclusion of kinematic
requirements would be necessary to improve kinematic symmetry as well.
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