Ultrasound based skeletal motion capture: the development and validation of a non-invasive knee joint motion tracking method

Kenan Niu

    Research output: ThesisPhD Thesis - Research UT, graduation UT

    208 Downloads (Pure)

    Abstract

    Musculoskeletal disorders, particularly in the lower limb, are the most common cause of severe long-term pain and physical disability, and affect hundreds of millions of people around the world. Accurate measurement tools are required to diagnose these pathologies and to evaluate the efficacy of various treatment options. In this respect, detailed measurement and analysis of human movement have shown to be of great value. The goal of this research was to develop and validate a new non-invasive method based on ultrasound that is able to track skeletal motion around the knee joint and to quantify tibiofemoral kinematics under dynamic conditions. To achieve this goal we separated the work in various parts which are outlined in this thesis. Average rotational errors of 1.51 ± 1.13° (mean ± SD) and average translational errors of 3.14 ± 1.72 mm (mean ± SD) were obtained. In conclusion, ultrasound based skeletal motion capture is feasible and has the potential to achieve high accuracy in the estimation of skeletal motion and quantification of 6-DOF joint kinematics. The currently developed system showed the ability to directly measure skeletal kinematics despite soft tissue deformations between the transducer and the bony surface. Therefore, this method has great potential to be considered as a suitable alternative for measuring human skeletal motion. In this thesis a considerable number of improvement steps are described which will enable to achieve higher accuracies and sampling rates than those described in this thesis. After implementation of these improvements a unique measurement system can be obtained that can be applied to a variety of applications such as quantification of dynamic motion and deformation of soft tissue structures, general gait analysis, prosthetic design optimization, orthopaedic reconstructive surgery and surgical navigation. This thesis provides the foundation for these future applications.
    Original languageEnglish
    Awarding Institution
    • University of Twente
    Supervisors/Advisors
    • Verdonschot, Nicolaas Jacobus Joseph, Supervisor
    • Homminga, J,J., Co-Supervisor
    • Sprengers, A.M.J., Supervisor
    Award date15 Feb 2018
    Place of PublicationEnschede
    Publisher
    Print ISBNs978-90-365-4482-5
    DOIs
    Publication statusPublished - 15 Feb 2018

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