Design of Real-Time Hip Joint Simulation

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Abstract

This thesis aims to visualize a hip joint and its motion and use machine learning to predict the pathology of a hip joint given its movement during an average gait cycle using MATLAB in hopes of assisting clinicians in diagnosing hip conditions.

There is a lack of software that can assist health practitioners and researchers visualize a patient’s hip joint. The created simulation utilizes data from user-uploaded STL files on the femur and acetabulum. 3D joint models of patients were given to test and train the classifier aspect of the program.

The software allows the user to rotate the femur bone along the sagittal, frontal, and transverse planes while dynamically displaying the interference points. The software calculates the interference points, the center of rotation, and the axes of rotation. Calculating the interference points between the femoral head and the acetabulum is the foundation of the software. The software identifies interference points between the femoral head and the acetabulum. These points are then fitted to a sphere representing the femoral head. This approach defines the center of rotation as the center of the sphere, providing a crucial foundation for visualizing the hip joint and its motion. The next step was to calculate the axes of rotation for each of the three planes; the software utilizes global axes of rotation.

The program utilizes gait motion data to provide additional insight into the features of each joint by displaying an animated gait cycle. The software simulates the joint, and it can be animated and show the interference points at each step of the gait cycle. This visualization can show what the interference of an abnormal hip would look like during a normal gait cycle.

Finally, the software has a classification model to produce a predicted pathology. The classifier was trained using various features of the femur and acetabulum through an average gait cycle. The classifier utilizes these features to analyze the uploaded model and provide a pathology prediction to the user.

Cited Works (2)

Year	Entry
2014	Kapron AL, Aoki SK, Peters CL, Maas SA, Bey MJ, Zauel R, Anderson AE. Accuracy and feasibility of dual fluoroscopy and model-based tracking to quantify in vivo hip kinematics during clinical exams. J Appl Biomech. June 2014;30(3):461-470.
2008	Silverman AK, Fey NP, Portillo A, Walden JG, Bosker G, Neptune RR. Compensatory mechanisms in below-knee amputee gait in response to increasing steady-state walking speeds. Gait Post. November 2008;28(4):602-609.