1,191 research outputs found
Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.
Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations
Task and Configuration Space Compliance of Continuum Robots via Lie Group and Modal Shape Formulations
Continuum robots suffer large deflections due to internal and external
forces. Accurate modeling of their passive compliance is necessary for accurate
environmental interaction, especially in scenarios where direct force sensing
is not practical. This paper focuses on deriving analytic formulations for the
compliance of continuum robots that can be modeled as Kirchhoff rods. Compared
to prior works, the approach presented herein is not subject to the
constant-curvature assumptions to derive the configuration space compliance,
and we do not rely on computationally-expensive finite difference
approximations to obtain the task space compliance. Using modal approximations
over curvature space and Lie group integration, we obtain closed-form
expressions for the task and configuration space compliance matrices of
continuum robots, thereby bridging the gap between constant-curvature analytic
formulations of configuration space compliance and variable curvature task
space compliance. We first present an analytic expression for the compliance of
a single Kirchhoff rod. We then extend this formulation for computing both the
task space and configuration space compliance of a tendon-actuated continuum
robot. We then use our formulation to study the tradeoffs between computation
cost and modeling accuracy as well as the loss in accuracy from neglecting the
Jacobian derivative term in the compliance model. Finally, we experimentally
validate the model on a tendon-actuated continuum segment, demonstrating the
model's ability to predict passive deflections with error below 11.5\% percent
of total arc length
Development of a decision support system for the design and adjustment of sailboat rigging
The two main objective of this work are:
- To develop a simulation program of the behaviour of upwind sails and
rigging, to help the crew to optimize the performance of the sailing yacht in
real time. For this purpose, it will be necessary to formulate a fluid-structure
interaction algorithm to compute the performance of a particular sail/rigging
configuration. Since the crew dynamically trims the rigging and sails, in order
to evaluate the performance of the actual configuration, a tool to monitor
the rigging and sails will be necessary, too.
- To adjust a monitoring element to quantify in physic values the manoeuvre of
the crew. This will be our monitoring tool.
- To reproduce the crew manoeuvre in the simulation program with the data
obtained with the monitoring tool. Once the sail/rigging configuration has
been adapted ‘in real time’ to the actual one, the performance of this new
configuration can be computed. For this purpose the simulation program and
the monitoring tool must communicate among them
- …