Anterior cruciate ligament (ACL) injury is a major public health issue. Incidence of ACL rupture is high and surgery and rehabilitation is costly with conservative estimates ranging from $17-
$25,000 per injury (de Loes et al., 2000; Hewitt et al., 1999). These costs do not account for the increased risk of developing osteoarthritis following ACL rupture (Ruiz et al., 2002) and its’ associated health care consequences. There is consensus in the literature that the majority of ACL ruptures (~70%) are non-contact, occurring when an athlete makes a sudden change in direction or lands from a jump (McNair et al, 1990). Anterior cruciate ligament injury occurs with a 4-6fold greater incidence in female athletes compared with male athletes playing the same landing and cutting sports (Arendt & Dick, 1995). The reason for this gender disparity is still unclear, although factors such as anatomical, hormonal, and biomechanical differences have all been suggested. Identification of individuals ‘at-risk’ and implementation of appropriate intervention strategies to reduce risk of injury are clearly warranted.
Research efforts have focused on understanding the mechanism of ACL rupture, as this knowledge is imperative to designing preventative and rehabilitation strategies. Rupture of the
ACL occurs when the load applied to the ligament exceeds the failure strength of the tissue. The magnitude of ACL load is determined by the external loads (moments) applied to the knee joint, the contributing support by muscles and other soft tissue structures, and the posture and geometry of the knee joint when the load is applied (Figure 1). Potential intervention strategies should then focus on several areas:
1.
Reducing the external load applied to the knee joint
2.
Improving muscular support of the joint
3.
Changing the posture of the knee joint when the joint is loaded
4.
Increasing ligament strength
External Loading of the Knee during Cutting and
Landing Manoeuvres
External loads that apply anterior draw of the tibia, varus-valgus, and internal rotation of the tibia with respect to the femur are known to increase strain on the ACL (Markolf et al., 1995).
Figure 1. Mechanism of ACL rupture
Large varus-valgus and internal rotation moments are applied to the knee during early stance phase of sidestep cuttings tasks (Besier et al., 2001a) and landing from a jump and are a likely mechanism for non-contact ACL rupture. A more recent study showed that knee valgus moments coupled with knee valgus angle and ground contact times during a drop jump task were significant predictors of future ACL injury risk (Hewett et al.,
2005).
Varus-valgus and internal rotation moments increase substantially (up to two-fold) when an athlete makes a sudden change in direction in response to a light stimulus (Besier et al., 2001b).
It is interesting to note that some athletes experience valgus loads applied to the knee while

others experience varus loads when performing the same cutting task (Besier et al., 2001a).
Varus-valgus loading at the knee is influenced by the orientation of the lower limb upon landing and the accelerations of the body segments when the foot is in contact with the ground, suggesting that technique modifications can be made to alter the loads at the joint and reduce risk of injury. Anticipation of an up-coming movement allows an athlete to adjust their posture to reduce the loads experienced at the knee joint (Besier et al., 2001b), highlighting the importance of visual perception training as a modality to reduce risk of knee injury.
Muscular Support of the Knee Joint
All muscles surrounding the knee have the potential to support flexion-extension, varus-valgus, and internal-external rotation loads, by virtue of their moment arms. The question is whether these muscles are activated appropriately to support these external loads during dynamic tasks.
During cutting tasks, muscle activation strategies are pre-programmed by the central nervous system to the external load that is anticipated (Besier et al., 2003). For example, when externally applied valgus loads are applied to the knee joint, the activation of muscles that produce varus loads increase, thus stabilizing the joint (Besier et al., 2003). Selected activation of muscles is one common mechanism that the central nervous system uses to support external loads at the knee.
The other mechanism is co-contraction or co-activation of muscles. At the knee joint, the most potent muscle co-contraction pattern is that of the quadriceps and hamstrings (Lloyd and
Buchanan, 2001; Lloyd et al., 2005). These muscle groups produce the greatest amount of force at the knee joint and have moment arms to support varus-valgus and internal-external rotation moments at the knee. Therefore, at the same time as producing extension and flexion at the knee joint, the quadriceps and hamstrings provide significant support for varus-valgus and internalexternal rotation at the knee (Solomonow et al., 1987). Hamstring-quadriceps co-contraction occurs during cutting tasks to support varus-valgus and internal-external rotation loads (Besier et al., 2003).
Role of Knee Posture in Influencing ACL Load
The posture of the knee when external loads are placed on the joint has an influence on how much strain is placed on the ACL. At extended knee postures, the ligaments of the knee become taut and additional varus-valgus or rotation load increase the strains placed on the ACL (Markolf et al., 1995). Video analysis of athletes rupturing their ACL has revealed that it is common for athletes to land with the knee near full extension and collapse into valgus (Boden et al., 2000). As a rule of thumb, athletes should be encouraged to increase knee flexion when performing cutting and landing manoeuvres.
Increasing Ligament Strength
Ligaments, like most load-bearing biological tissue, have the capability to adapt to their mechanical environment, potentially altering their composition and microstructure. Animal studies have shown that, with immobility, ligaments lose mechanical integrity relatively quickly, and upon return to mobility, restoration of properties proceeds at a slower rate (Woo et al.,
1987). It remains to be seen whether human ACL strength can be improved with training, although the implication that ligaments remodel based on its load history suggests that if one can develop an intervention to load the ACL in an appropriate manner, its’ failure strength will improve.

Efficient computation of multi-body dynamics and neuromuscular control will enable the realtime estimation of important variables such as joint kinematics, kinetics, muscle forces, and potentially, tissue-level stresses and strains. Information regarding these variables may then be given back to an athlete via skin stretch to alter his or her technique to perform a task.
To begin with, an experimental protocol designed to reduce valgus knee joint loads can be developed.
Experiment 1: Can skin stretch feedback alter valgus knee loads?
1.
Athlete performs simple drop jump landing task in motion capture laboratory. Following data are collected: a.
Marker trajectories b.
Ground reaction forces
2.
Create scaled anatomical model of subject
3.
Use human motion reconstruction algorithm to match dynamics of the task and predict ground reaction forces
4.
Given bounds and constraints (e.g. strength and flexibility), simulate a novel optimal performance for that subject that minimizes valgus moments applied to the knee
5.
Provide real-time skin stretch feedback of varus-valgus loads and see if athlete then uses this information to reduce valgus loads through trial and error
6.
Compare the athletes new technique with the predicted ‘optimal technique’
Experiment 2: Compare skin stretch to a verbal cue
1.
Athlete performs sidestep or landing task in motion capture laboratory. Following data are collected: a.
Marker trajectories b.
Ground reaction forces
2.
Create scaled anatomical model of subject
3.
Use human motion reconstruction algorithm to match dynamics of the task
4.
Given bounds and constraints (e.g. strength and flexibility), simulate a novel optimal performance for that subject that minimizes valgus moments applied to the knee
5.
Identify variables that best characterize difference between actual performance and the novel optimal task and translate the variable(s) to a verbal cue (e.g. “Keep trunk upright” or “keep the knee aligned over the ankle”)
6.
Use verbal cue to alter athletes’ performance OR use skin stretch to provide feedback on a variable of interest (e.g. knee valgus moment)
7.
Compare differences between verbal cue and skin stretch intervention
To perform these experiments, several steps need to be tested and validated:
1.
Interface required between VICON motion capture system and dynamics environment
2.
Validation of predicted and measured ground reaction forces (contact model formulation)

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