Self-Expressive Subspace Clustering to Recognize Motion Dynamics of a Multi-Joint Coordination for Chronic Ankle Instability

Abstract: Ankle sprains and instability are major public health concerns. Up to 70% of individuals do not fully recover from a single ankle sprain and eventually develop chronic ankle instability (CAI). The diagnosis of CAI has been mainly based on self-report rather than objective biomechanical measures. The goal of this study is to quantitatively recognize the motion pattern of a multi-joint coordination using biosensor data from bilateral hip, knee, and ankle joints, and further distinguish between CAI and healthy cohorts. We propose an analytic framework, where a nonlinear subspace clustering method is developed to learn the motion dynamic patterns from an inter-connected network of multiply joints. A support vector machine model is trained with a leave-one-subject-out cross validation to validate the learned measures compared to traditional statistical measures. The computational results showed >70% classification accuracy on average based on the dataset of 48 subjects (25 with CAI and 23 normal controls) examined in our designed experiment. It is found that CAI can be observed from other joints (e.g., hips) significantly, which reflects the fact that there are interactions in the multi-joint coordination system. The developed method presents a potential to support the decisions with motion patterns during diagnosis, treatment, rehabilitation of gait abnormality caused by physical injury (e.g., ankle sprains in this study) or even central nervous system disorders.