ORCID ID
Graduation Date
Spring 5-7-2022
Document Type
Thesis
Degree Name
Master of Science (MS)
Programs
Medical Sciences Interdepartmental Area
First Advisor
Elizabeth Wellsandt
Second Advisor
Eric Markvicka
Third Advisor
Matthew Tao
Fourth Advisor
Gregory Bashford
MeSH Headings
Wearable Electronic Devices, Biomechanical Phenomena, Knee Joint, Range of Motion, Algorithms
Abstract
The measurement of three-dimensional knee joint angles can predict both anterior cruciate ligament (ACL) injuries and the risk of developing early knee osteoarthritis. However, knee joint angle assessment is currently limited, due to the lack of validated wearable and untethered technologies that can be deployed in natural environments and rural or community settings. To address this challenge, this thesis project aimed to 1) develop a fully untethered, wearable electronic device to measure knee joint angles during natural human movement and 2) test the accuracy of the device to collect sagittal knee joint angles during dynamic activities of human movement. For Aim 1, we created a fully untethered, wearable electronic device to continuously measure knee joint angles using two inertial measurement unit sensors. The device is composed of a stretchable circuit assembled on spandex-blend fabric, allowing the device to conform to the knee without restricting natural movement. Cyclic loading testing suggests the device can endure more than 3000 cycles to 125° knee flexion. For Aim 2, we validated our device to collect sagittal-plane knee angles against an 8-camera Qualisys motion capture system during knee extension in the seated position, bilateral squats, and bilateral drop vertical jumps. Our preliminary data in three individuals demonstrated good average root-mean-square deviations (RMSD) during knee extensions, but poorer RMSD values during squats and drop jumps. The device was shown to collect accurate sagittal knee joint angles during knee extensions, but further investigation is required to validate accurate sagittal knee angles during squats and drop jumps.
Recommended Citation
McManigal, Matthew, "Mobile, Biosensor Technology for Measuring Joint-Level Human Motion" (2022). Theses & Dissertations. 635.
https://digitalcommons.unmc.edu/etd/635