Body
Quantitative movement analysis normally requires a motion laboratory with marker-based cameras and force plates, which limits how often it can be used and keeps it out of routine clinical care. This program develops camera-based methods that measure movement quality outside the laboratory.
The lab uses OpenCap, an open-source system that reconstructs three-dimensional movement from two synchronized smartphone videos. Computer vision estimates body keypoints from the video, and musculoskeletal modeling converts them into joint kinematics and estimates of the underlying kinetics. Within this program, the lab developed a Gait Deviation Index for MS, a single score that summarizes the difference between an individual's gait kinematics and a normative pattern, and tested its reliability and its sensitivity to change in a physical therapy clinic.
Common clinical measures of walking, such as speed and endurance, do not capture how the limb moves, and this index provides a measure of walking quality that can be collected during a routine visit. The lab is extending these methods across MS, spinal cord injury, and stroke, which makes it possible to track movement quality repeatedly over the course of an intervention rather than only before and after.