  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.
