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Articles

A real-time computational model for estimating kinematics of ankle ligaments

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Pages 835-844 | Received 26 Jan 2015, Accepted 16 Jun 2015, Published online: 07 Aug 2015
 

Abstract

Background: An accurate assessment of ankle ligament kinematics is crucial in understanding the injury mechanisms and can help to improve the treatment of an injured ankle, especially when used in conjunction with robot-assisted therapy. A number of computational models have been developed and validated for assessing the kinematics of ankle ligaments. However, few of them can do real-time assessment to allow for an input into robotic rehabilitation programs. Method: An ankle computational model was proposed and validated to quantify the kinematics of ankle ligaments as the foot moves in real-time. This model consists of three bone segments with three rotational degrees of freedom (DOFs) and 12 ankle ligaments. This model uses inputs for three position variables that can be measured from sensors in many ankle robotic devices that detect postures within the foot–ankle environment and outputs the kinematics of ankle ligaments. Validation of this model in terms of ligament length and strain was conducted by comparing it with published data on cadaver anatomy and magnetic resonance imaging. Results: The model based on ligament lengths and strains is in concurrence with those from the published studies but is sensitive to ligament attachment positions. Conclusions: This ankle computational model has the potential to be used in robot-assisted therapy for real-time assessment of ligament kinematics. The results provide information regarding the quantification of kinematics associated with ankle ligaments related to the disability level and can be used for optimizing the robotic training trajectory.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

Additional information

Funding

This material was based on work supported by the University of Auckland, Faculty of Engineering Research Development Fund [grant number 3625057] (Physical Robot-Human Interaction for Performance-Based Progressive Robot-Assisted Therapy) and China Sponsorship Council.

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