Performing exercises, especially cutting and pivoting activities, with poor lower extremity mechanics can lead to severe damage of the knee, such as anterior cruciate ligament (ACL) tears [1]. A common movement pattern observed in at-risk athletes is knee valgus. This term refers to the medial collapse of the knee (when the knees falls inward towards the center of the body). Intervention to prevent knee valgus could reduce the chance of injury for at-risk athletes, or re-injury for those recovering from a knee injury.
Currently, in patients with knee injuries, knee valgus is monitored by physical therapists, who observe a patient’s movements visually during exercise. The therapists instruct patients on how to identify valgus and how they might correct it. Visual diagnosis of valgus can be difficult and subjective, thereby allowing the unavoidable presence of human error. In addition, monitoring in real time is only possible when the patient is with a therapist. Several studies have focused on the issue of accurate detection of knee valgus by using a variety of systems such as 2D and 3D motion capture systems to track knee and hip movements, dynamometers, and electromyography [2][3][4]. Although these systems are able to determine knee valgus, they are difficult to use, require expensive equipment, and do not provide real-time feedback outside of the clinic setting. The purpose of this study was to inform the design of a valgus-sensing legging by exploring sensor placement options to maximize the magnitude of the sensor response difference between valgus and non-valgus knee bends.
