Clinical Parameters Prediction for Gait Disorder Recognition
This work addresses gait disorder diagnosis for patients, but it appears incremental as it applies existing methods to a specific medical domain without clear novelty in approach.
The paper tackles the problem of predicting clinical parameters for gait disorder diagnosis by using both clinical measurements and video-extracted joint coordinates, and then uses these predictions to pre-classify disorder intensity and decide on treatment needs.
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.