Motion Analysis in Anterior Cruciate Ligament Deficient and Reconstructed Knees

Introduction

Anterior cruciate ligament (ACL) rupture is a serious injury of the knee joint that results in instability. The stability of the knee thought to have an ACL injury is traditionally evaluated with an arthrometer (i.e., KT-1000) while the patient is relaxed in a supine position. The arthrometer provides the clinician with a quantitative measure of the amount of passive anterior translation between the femur and the tibia. A minimal amount of joint laxity (within 3 mm of the contralateral knee with intact ACL) during the test is clinically and functionally acceptable. However, such an evaluation is a measure of passive joint stability and does not necessarily reflect the joint’s stability during daily physical activities. Dynamic functional joint stability , on the other hand, is defined as the condition in which the joint is stable during daily physical activities. Previous research has indicated that there is lack of a relationship between passive and dynamic functional joint stability.

To address the limitations of passive stability testing, motion analysis has been used to quantify the dynamic functional knee stability in ACL-deficient (ACL-D) and ACL-reconstructed (ACL-R) knees. Motion analysis is an advanced laboratory process by which present day electronics (i.e., video cameras) are used to integrate information from a variety of inputs in order to demonstrate and analyze the dynamics of functional activities such as gait, jump-landing, or pivoting ( Fig. 139.1 ).

Motion analysis has its roots in the early investigations of Eadweard J. Muybridge’s series of photographs in The Horse in Motion , which challenged common beliefs by demonstrating that all hooves leave the ground when they are tucked under the running horse. Current advanced, high-accuracy motion analysis systems have provided new insights and commonly disproved theories that were widely accepted. The advantages of these systems include the ease of application, safety, and quick data processing that frequently allows real-time viewing and immediate replay, while the main disadvantage is that there is relative motion between the markers and the skin. Kinematic data collection (the study of moving bodies without regard to forces) is primarily achieved with the use of high-speed and high-resolution cameras and skin markers in a laboratory environment. The markers can be either passive (reflecting light; e.g., Vicon or Motion Analysis systems) or active (generating light; e.g., Optotrak). Kinetic data are collected with the use of force transducers, commonly a force plate installed flush with the floor. As the subject performs a task on the force plate, ground reaction forces are calculated. With the use of inverse dynamics, kinetic and kinematic data can be combined to calculate joint forces and moments.

To improve the fidelity of kinetic and kinematic data collected during motion analysis, careful experimentation procedures need to be followed: (a) minimize interoperator error by having the same clinician place all markers and acquire all anthropometric measurements, (b) use a standing calibration procedure to correct for subtle misalignment of the markers that define the local coordinate system and to provide a definition of zero degrees for all movements in all planes, (c) maximize control conditions to test for true differences. (i.e., collect data from the intact leg of the ACL-R or ACL-D group and a healthy group of individuals with no history of knee injury), (d) always use the same instrumentation for all participants to maintain the same level of measurement noise, (e) increase statistical power by using an adequate sample size that is calculated by an a priori power analysis, (f) mark the location of markers when testing the same participants on different days, and (g) utilize current software to perform inverse dynamic calculations.

Reliability and validity studies with recent versions of motion analysis systems have shown that the errors generated are usually small enough to allow for accurate data interpretation. Even better validity is achieved with the use of intracortical pins that directly insert into bony segments and are attached to clusters of markers ; however, the invasiveness of this methodology has made it less popular than skin markers. Other methods of collecting kinematic data include accelerometers, electrogoniometers, electromagnetic devices, and fluoroscopy. In recent cases, important information on the mechanism of ACL injuries has been provided by data from injuries that were captured on video and subsequently analyzed. The future of in vivo biomechanical research is exciting, as new technologies have emerged that allow collection of data outside the laboratory, which opens up new avenues for research in more ecological environments such as the clinic or the sports field. These systems use either cameras that have been optimized for outdoor tracking of skin markers (e.g., Qualisys, Vicon) or markerless systems, and although promising, currently very few studies have utilized them.

In vivo biomechanics are nowadays used as a valuable tool for orthopaedic surgeons, physical therapists, and other members of the rehabilitation team involved in the prevention and treatment of ACL injuries. In recent years, important, clinically relevant biomechanical studies have greatly enhanced our understanding of the ACL-D and ACL-R knee and have influenced surgical and rehabilitation protocols. Motion analysis allows the in vivo evaluation of the ACL-D and ACL-R knee during dynamic activities (i.e., walking, pivoting, landing from a jump), something that static measures (i.e., arthrometer) are unable to do. In the following sections, we will discuss the most important adaptations after ACL injury and reconstruction, and the research findings from motion analysis studies.