# Clinical Parameters Prediction for Gait Disorder Recognition

*denotes equal contribution

Abstract - 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 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 afterward 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 patients' 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.

Keywords: Machine learning, Computer vision, Gait disorder, Automatic Disease Diagnosis

Highlights

Fig.1. Numbering of joints of the body by OpenPose - $18$ different joints

Fig.2. Example: x-coordinate of the nose from two camera views appeared in JSON files for 2000 frames before cleaning

Fig.3. Example: x-coordinate of the nose from two camera views appeared in JSON files for 2000 frames after cleaning

Demonstration of OpenPose in capturing the motion of different joints of the body in front of a camera