On Discovering the Correlated Relationship between Static and Dynamic Data in Clinical Gait Analysis

‘Gait’ is a person’s manner of walking. Some patients may have an abnormal gait due to physical impairment. Clinical gait analysis (CGA) is a practical technique and procedure for identifying the underlying impairments that affect the pattern of a patient’s gait. The CGA is critical for further treatment planning. Essentially, CGA tries to use patients’ physical examination results, known as static data, to interpret the dynamic characteristics in an abnormal gait, known as dynamic data. For many years this process has been qualitatively carried out by gait analysis experts, based mainly on their experience which may lead to subjective diagnoses. To facilitate the automation of this process and form a relatively objective diagnostic view, this paper proposes a new probabilistic correlated static-dynamic model (CSDM) to discover quantitatively correlated relationships between the dynamic characteristics of the gait and their root cause in the static data space. We propose an EM-based algorithm to learn the parameters of the CSDM. One of the main advantages of the CSDM is its ability to provide intuitive knowledge. For example, the CSDM can describe what kinds of static data will lead to what kinds of hidden gait patterns in the form of a decision tree, which helps us to infer what kind of dynamic characteristics would be given to the static data. Our initial experiments indicate that the CSDM is promising for discovering the correlated relationship between the physical examination (static) and gait (dynamic) data.