Research Interests

Our research focuses on human movement biomechanics and lower-extremity musculoskeletal conditions, with a particular emphasis on knee and patellofemoral pain. We investigate how movement patterns, muscle characteristics, and mechanical loading contribute to pain, injury, and changes in physical function during walking, running, jumping, and other functional activities.

To address these questions, we integrate motion capture, wearable sensors, plantar-pressure assessment, musculoskeletal imaging, computational modeling, and machine-learning methods. This combination allows us to examine movement and loading in laboratory, athletic, clinical, and real-world environments.

Our long-term goal is to identify meaningful biomechanical markers of musculoskeletal health and translate them into accessible digital health approaches for individualized assessment, rehabilitation, and injury prevention.

 

Current Research Projects:

Quantifying Muscle Volume and Asymmetry in Athletes with Patellofemoral Pain

Patellofemoral pain can affect athletic performance, physical function, and long-term joint health. We use freehand three-dimensional ultrasound (3DUS) to quantify gluteal muscle volume and examine differences between individuals with and without patellofemoral pain and between limbs. This research aims to clarify the role of muscle morphology and asymmetry in patellofemoral pain and recovery.

Funded by the Louisiana Board of Regents Research Competitiveness Subprogram, June 2024–June 2027.

 

 

Wearable-Sensor Assessment of Patellofemoral Pain Using Machine Learning

We investigate whether wearable sensors—including inertial measurement units and plantar-pressure insoles—can identify movement and loading patterns associated with patellofemoral pain. Data collected during walking, running, and functional tasks are analyzed using machine-learning methods to evaluate whether individual sensors or combinations of sensors provide the most informative assessment.

This work supports the development of noninvasive, field-deployable digital health tools for objective movement assessment outside traditional biomechanics laboratories.

The project involves interdisciplinary collaboration with clinical partners, LSU Athletics, computer science, and experimental statistics.

Funded by the Louisiana Board of Regents Research Competitiveness Subprogram, June 2024–June 2027.

   

 

Emerging Research Directions

Our emerging research integrates longitudinal human-movement data, musculoskeletal imaging, and computational methods to support individualized rehabilitation and musculoskeletal-health assessment.

 

 

Prospective Assessment of Musculoskeletal Injury Risk

We are developing prospective studies to determine whether biomechanical, muscular, and wearable-sensor measures can identify changes that precede lower-extremity musculoskeletal injuries. This work will follow athletes over time and support the development of individualized approaches to injury-risk assessment and prevention.

Digital Twin Modeling for Individualized Musculoskeletal Assessment

In collaboration with researchers in computer science, we are developing a digital twin framework that integrates individual-level biomechanical, wearable-sensor, and musculoskeletal imaging data. The study investigates whether personalized computational models can characterize movement and tissue-loading patterns associated with lower-extremity musculoskeletal conditions.

By combining experimental data with computational modeling and machine-learning methods, we aim to evaluate how an individual’s movement and loading patterns may change under different conditions. This work represents an emerging digital health approach to individualized musculoskeletal assessment and, ultimately, rehabilitation planning.

Computational Modeling of Lower-Extremity Joint Loading

We are expanding our use of computational modeling to examine how alterations in movement and muscle function influence lower-extremity joint loading. By integrating biomechanical data and musculoskeletal modeling, we aim to improve our understanding of the mechanisms underlying musculoskeletal pain and injury.

Research Opportunities

Undergraduate, master’s, and doctoral students interested in human movement biomechanics, wearable technology, musculoskeletal imaging, computational modeling, or machine learning are encouraged to contact Dr. Kim. Experience with programming, data analysis, athlete testing, or longitudinal research is valuable but not required.