Self-expressive subspace clustering to recognize motion dynamics for chronic ankle instability

Abstract

Ankle sprains and instability are major public health concerns. Up to 70% of individuals do not fully recover from single ankle sprains 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 patterns of a multi-joint coordinate system using gait data of bilateral hip, knee, and ankle joints, and further distinguish CAI from control cohorts. We propose an analytic framework, where the concept of subspace clustering is applied to characterize the dynamic gait patterns in a lower dimensional subspace from an inter-dependent network of multiply joints. A support vector machine model is built to validate the learned measures compared to traditional statistical measures in a leave-one-subject-out cross validation. The experimental results showed >70% classification accuracy on average for the dataset of 47 subjects (24 with CAI and 23 controls) recruited to examine 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 exists inter-dependency in the multi-joint coordinate system. The proposed framework presents a potential to support clinical decisions using quantitative measures during diagnosis, treatment, rehabilitation of gait abnormality caused by physical injuries (e.g., ankle sprains in this study) or even central nervous system disorders.

Keywords:

• Chronic ankle instability
• gait data analysis
• subspace learning
• pattern recognition
• decision model

Acknowledgements

This work is supported by Northeastern TIER 1 Seed Grant. The authors sincerely thank editors and anonymous reviewers for constructive suggestions and comments to ensure the quality of this paper.