Osteoarthritis affects over 600 million people worldwide, with knee OA being the most common. Our lab investigates how movement impacts musculoskeletal and joint health by accounting for individual variation and multi-dimensional, time-varying patterns of movement that the body experiences in everyday life. We use markered and markerless motion capture systems, force platforms, wearable sensors, UF's HiPerGator supercomputer, and machine learning approaches.
Our lab investigates risk factors for knee osteoarthritis progression, working to predict disease advancement using accelerometer data and ground reaction forces. By identifying movement patterns associated with faster joint degeneration, we aim to inform early interventions that slow or prevent disease progression.
Large observational study with approximately 4,000 participants. The dataset includes patient metadata, clinical outcome data, accelerometer data, and ground reaction force data. Our lab pursues two analytical directions: examining physical activity as a risk factor for OA progression, and analyzing raw ground reaction force waveforms to identify biomechanical predictors of disease advancement.
Collects walking and daily activity data from healthy individuals and those with knee osteoarthritis. This study aims to identify movement patterns linked to disease progression using machine learning techniques applied to wearable sensor and motion capture data.
Examines how patterns of physical activity and OA pain flares correlate over time. This study analyzes dynamic activity characteristics including magnitude, duration, and loading frequency to understand how daily movement behaviors influence pain episodes in individuals with knee osteoarthritis.
A pilot project comparing community-based versus lab-based data collection approaches. This study uses markerless motion capture from video data to evaluate the feasibility of remote biomechanical assessment and whether community-based methods can improve participant diversity.
Our lab investigates biomechanics following total hip arthroplasty (THA), focusing on how patients move during functional activities after joint replacement surgery. We aim to provide evidence-based guidelines for safe return to physical activity and improve post-surgical outcomes.
Compares markerless versus marker-based motion capture during yoga poses. The goal is to validate markerless technology as a more comfortable alternative for patients while maintaining the accuracy needed for clinical biomechanical assessment.
No uniform clinical guidelines currently exist for returning to yoga following hip surgery. This study quantifies lower limb joint angles during yoga poses in post-THA patients compared to healthy controls, providing data to inform safe movement recommendations. Collaboration with Dr. Simon Mears, MD, PhD.
Our sports biomechanics research focuses on preventing osteoarthritis through athletic performance analysis. By understanding the forces and movement patterns involved in sport-specific activities, we can identify injury risk factors and develop training strategies that protect long-term joint health.
Develops a quantifiable relationship between force production and swing power. Force plate measurements are taken in the weight room and swing metrics are captured in the batting cage to create individual swing power projections. Collaboration with UF Softball and UF Strength & Conditioning.