Research Interests

Our research focuses on human movement biomechanics and lower-extremity musculoskeletal injuries, with a particular interest in knee pain and patellofemoral pain. We study how movement patterns, muscle function, and mechanical loading are related to injury risk during walking, running, jumping, and other functional movements.

To address these questions, we use motion capture, wearable sensors, plantar pressure assessment, medical imaging, computational modeling, and AI-based approaches. These tools allow us to examine movement and loading patterns in both laboratory and applied settings.

Our long-term goal is to identify biomechanical factors associated with knee injury risk and develop practical approaches for early assessment and prevention.

 

Current Research Projects:

Quantifying Muscle Volume and Asymmetry in Athletes with Patellofemoral Pain

Patellofemoral pain significantly impacts athletic performance and long-term joint health. We use freehand 3D ultrasound (3DUS) to quantify gluteal muscle volume. By identifying asymmetries between groups and between legs, we aim to determine which muscles play a critical role in injury prevention and recovery. We are validation of 3DUS against MRI to ensure accuracy and reliability in these assessments.

Board of Regents, RCS Funded Project. 6/2024-6/2027

 

 

Use of Wearable Sensors to Identify Individuals with Patellofemoral Pain Using a Machine Learning Approach

We are exploring the application of wearable sensors, including inertial measurement units (IMUs) and plantar pressure insoles, to identify individuals with patellofemoral pain. By collecting movement and pressure data during various locomotion tasks, machine learning algorithms will be trained to detect biomechanical patterns and asymmetries associated with PFP. This approach aims to provide a non-invasive, real-time diagnostic tool, enabling objective assessment and early detection of PFP in diverse environments outside traditional laboratory settings.

These projects involve collaboration with Ochsner clinics, LSU Athletics, the Computer Science department, and the Experimental Statistics department.

Board of Regents, RCS Funded Project. 6/2024-6/2027

   

 

Future Resaerch Interests

Computation Modeling Study

We aim to examine the relationship between biomechanical alterations and joint loading in lower limb MSK injuries. Our research focuses on leveraging tools like OpenSim and machine learning to deepen our understanding of these relationships. Students with experience in OpenSim modeling or machine learning are encouraged to contact Dr. Kim directly for potential research opportunities.

Prospective Study of Biomechanical Risk in MSK Injuries

We are also interested in identifying biomechanical risk factors through a prospective approach. Prospective PhD students with strong communication skills, particularly in working with athletes, and the ability to organize long-term lab sessions are encouraged to contact Dr. Kim.