Concurrent Validation of an Artificial Intelligence-Based Motion Capture System Using A Common Functional Movement Screen Exercise

The ability to accurately measure and quantify human motion is essential in both sport performance and musculoskeletal injury rehabilitation and prevention. Electromyography is a common tool that is used to measure muscle activity and can be used to assess movement quality. However, due to limitations present when EMG is used in isolation, biomechanical analysis is often used concurrently to quantify movement. Currently, marker-based optical motion capture systems are the gold standard in motion analysis; but they are not always ideal due to significant barriers in set up and operation. Conversely, markerless motion capture systems, are claimed by their manufacturers to be more cost effective and user friendly. Currently, the forward lunge is a common exercise in both rehabilitative and human performance programs due to its unilateral nature, which has been shown to increase prime mover muscular strength, stabilizer muscle activity, and range of motion. Physical therapists may avoid implementing it as a strategy during lower body musculoskeletal rehabilitation protocols, particularly in anterior cruciate ligament rehabilitation, due to the high quadricepts to hamstrings ratio. The backward lunge is an alternative to the forward lunge. Therefore, the primary purpose of this dissertation was to compare the electromyographical and kinematic differences between lunges. A secondary purpose was to assess the validity of a markerless motion capture system in measuring joint angles of these lunge variations. The results of this dissertation showed that (Q:H) was higher in the forward lunge than the backward lunge. Kinematic data showed greater knee angle in the forward lunge. For the markerless system, moderate to excellent agreement was shown between systems for both lunges. This study shows that different variations of the lunge exercise result in different joint angles, which may impact muscle activity. Furthermore, a markerless system may be a useful tool in quantifying movement in clinical settings once system accuracy improves.