Understanding knee joint laxity in the clinical assessment of anterior cruciate ligament injuries
File(s)
Author(s)
Allott, Natasha Elizabeth Helena
Type
Thesis
Abstract
Anterior cruciate ligament (ACL) injuries are prevalent, debilitating, and impose significant costs on both patients and healthcare systems. Characterised by increased joint laxity and compromised stability, these injuries present substantial challenges in clinical diagnostics due to the inherent subjectivity and difficulty in quantifying joint laxity.
This thesis investigates current clinical pathways and diagnostic challenges associated with ACL injuries to identify areas for improvement. It evaluates kinematic variations between ACL-injured individuals and uninjured controls using optical motion capture during laxity tests and gait analysis. It also investigates muscle activity by analysing electromyography (EMG) activity during these assessments. Additionally, the potential of wearable sensors to detect kinematic markers of laxity during clinical evaluations is explored.
Through systematic and scoping reviews, as well as surveys, this research highlights the subjectivity in laxity assessment as a major barrier to accurate and timely diagnosis. A cohort study comprising measures of motion capture, EMG and IMU sensor data explores the kinematic differences between the ACL injured and healthy cohorts, with a view to facilitating the clinical diagnostic and management process. Motion capture data reveal statistically significant increases in anterior and anteromedial rotary laxity in ACL-injured individuals, with variations influenced by the specific clinical tests administered. Gait analysis further underscores distinct differences in kinematic behaviours, demonstrating altered walking patterns that reflect compensatory strategies to mitigate instability. EMG results suggest that different clinical tests affect muscle guarding responses, reinforcing the need for a battery of comprehensive joint laxity and functional movement assessments.
Finally, a machine learning model developed from IMU sensor data captured during laxity testing illustrates the diagnostic potential of wearable technologies. This model enhances diagnostic accuracy and offers a promising avenue to revolutionise patient care by integrating objective measures into routine clinical assessment.
This thesis investigates current clinical pathways and diagnostic challenges associated with ACL injuries to identify areas for improvement. It evaluates kinematic variations between ACL-injured individuals and uninjured controls using optical motion capture during laxity tests and gait analysis. It also investigates muscle activity by analysing electromyography (EMG) activity during these assessments. Additionally, the potential of wearable sensors to detect kinematic markers of laxity during clinical evaluations is explored.
Through systematic and scoping reviews, as well as surveys, this research highlights the subjectivity in laxity assessment as a major barrier to accurate and timely diagnosis. A cohort study comprising measures of motion capture, EMG and IMU sensor data explores the kinematic differences between the ACL injured and healthy cohorts, with a view to facilitating the clinical diagnostic and management process. Motion capture data reveal statistically significant increases in anterior and anteromedial rotary laxity in ACL-injured individuals, with variations influenced by the specific clinical tests administered. Gait analysis further underscores distinct differences in kinematic behaviours, demonstrating altered walking patterns that reflect compensatory strategies to mitigate instability. EMG results suggest that different clinical tests affect muscle guarding responses, reinforcing the need for a battery of comprehensive joint laxity and functional movement assessments.
Finally, a machine learning model developed from IMU sensor data captured during laxity testing illustrates the diagnostic potential of wearable technologies. This model enhances diagnostic accuracy and offers a promising avenue to revolutionise patient care by integrating objective measures into routine clinical assessment.
Version
Open Access
Date Issued
2024-09-11
Date Awarded
01/04/2025
License URL
Advisor
McGregor, Alison
Banger, Matthew
Publisher Department
Department of Surgery & Cancer
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)