  Being able to predict clinical parameters in order to diagnose gait disorders
in a patient is of great value in planning treatments. It is known that
\textit{decision parameters} such as cadence, step length, and walking speed
are critical in the diagnosis of gait disorders in patients. This project aims
to predict the decision parameters using two ways and afterwards giving advice
on whether a patient needs treatment or not. In one way, we use clinically
measured parameters such as Ankle Dorsiflexion, age, walking speed, step
length, stride length, weight over height squared (BMI) and etc. to predict the
decision parameters. In a second way, we use videos recorded from patient's
walking tests in a clinic in order to extract the coordinates of the joints of
the patient over time and predict the decision parameters. Finally, having the
decision parameters we pre-classify gait disorder intensity of a patient and as
the result make decisions on whether a patient needs treatment or not.
