Abstract: We apply novel techniques for analyzing accelerometry data to identify characteristics of free-living physical activity unique to individuals with lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). We have developed a novel set of features that characterize movement patterns in people with LSS and OA. These features are able to classify with 80% accuracy between groups, provide a novel way to look at physical performance.
Learning Objective 1: Understand new ways of looking at accelerometry signals to gain clinical insight into patient populations dealing with movement limiting disorders.
Justin Norden (Presenter)
Stanford University School of Medicine
Christy Tomkins-Lane, Mount Royal University
Aman Sinha, Stanford University
Richard Hu, University of Calgary
Mathew Smuck, Stanford University