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Trajectory planning of a robot for lower limb rehabilitation

Authors: Y. Pei; Y. Kim; Goro Obinata; Kazunori Hase; Dimitar H. Stefanov;

Trajectory planning of a robot for lower limb rehabilitation

Abstract

We introduce a method for lower-limb physical rehabilitation by means of a robot that applies preliminary defined forces to a patient's foot while moving it on a preliminary defined trajectory. We developed a special musculoskeletal model that takes into consideration the generated muscle forces of 27 musculotendon actuators and joint stiffness of the leg and allows the calculation of the motion trajectory of the robot and the forces that the robot needs to apply to the foot in each moment of the therapeutic exercise. Robotic treatment programs are customized for the individual patient by using a genetic algorithm (GA) that refers to the musculoskeletal model and calculates the parameters of the spline curves of the motion trajectory of the robot and forces acting on the foot.

Keywords

Leg, Movement Disorders, Movement, Motion Therapy, Continuous Passive, Robotics, Models, Biological, Therapy, Computer-Assisted, Humans, Computer Simulation, Muscle, Skeletal, Man-Machine Systems, Muscle Contraction

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Top 10%
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