Biomechanical insights to prevent and treat kneecap instability
Patellar instability, a condition associated with partial or full dislocation of the patella (kneecap), is common among adolescent athletes and can significantly impact mobility and long-term joint health. This project aims to identify the anatomical and biomechanical factors that contribute to patellar instability, with the goal of guiding personalized treatment and improving outcomes. Through this work, we aim to influence clinical decision-making and enhance injury prevention and treatment.
What you’ll do: You will work with real patient data, including medical images, clinical exam records, and survey responses. You’ll gain experience in medical image processing, machine learning and computer vision, biomechanical modeling and simulation, and statistical modeling to analyze knee structures and relate them to injury severity and treatment efficacy. No prior experience in these areas is required--Prof. Lee will provide mentorship and training.
Beyond this project, you’ll have the chance to help build the Harvey Mudd Biomechanics Lab’s capabilities by setting up and testing experimental biomechanics equipment, such as force plates, cameras, and wearable sensors. You may also engage with potential clinical and athletic collaborators to extend the lab’s impact in healthcare and sports performance.
Want to learn more? Prof. Lee will available in her office, Parsons 2363, during the engineering research open house on Thursday, 11/21, from 5:30-6:30pm! Please also feel free to email her at mrlee@hmc.edu or just drop by her office.
Essay prompt: Please introduce yourself and be sure to include: why you are interested in this project and what you hope to learn or achieve through this experience; how this opportunity aligns with your academic and career goals; and the amount of time you would be able to commit to the project in the spring semester and summer (as applicable). (Please keep your response to ~300-400 words.)
The Harvey Mudd Biomechanics Lab aims to improve health, mobility, and quality of life across the lifespan by studying human movement and performance. Our research uses a variety of techniques from biomechanical data science, musculoskeletal simulation, wearable sensing, medical imaging, statistical modeling, and machine learning to maintain and restore movement, reduce injury, improve treatment, and enhance performance.