Bahman Adlou, Ph.D.
Bio & Education
Bahman Adlou, Ph.D., is an Assistant Professor of Kinesiology at UNC Greensboro. He is a biomechanist studying how movement is organized and how it can be made more efficient. Efficiency means something different depending on who is moving. It appears as performance in a high-level athlete, as reduced risk of injury or falls in someone whose movement is declining, and as a faster return to function after injury, surgery, or a neurological event. Because the same biomechanical principles govern all three, his lab works across populations and species, including collegiate and elite athletes, youth and early sport specializers, recreational and everyday adults, older adults, and canine subjects.
Movement science now generates high-rate data from force plates, instrumented treadmills, optical and markerless motion capture, inertial sensors, EMG, and EEG. Most researchers depend on vendor software to make that data interpretable, which constrains the questions they can ask. Dr. Adlou writes his own analysis pipelines and fuses raw signals across instruments, applying machine learning to characterize movement in more dimensions than any single device captures, and to measure it during the tasks and in the settings relevant to the person being tested.
Before joining UNCG, he led biomechanics research at the U.S. Soccer Federation within its Performance Innovation group. He earned his Ph.D. at Auburn University and welcomes inquiries from prospective doctoral students and from collaborators at UNCG and elsewhere.
Education
- Ph.D., Kinesiology (Biomechanics), Auburn University
- Graduate Certificate, Data Engineering, College of Engineering, Auburn University
- M.Sc. Musculoskeletal Sport Science and Health, Loughborough University
- B.S., Biological Sciences, University of California, Irvine
- B.A., Economics, University of California, Irvine
Research Interests
- Biomechanics of human movement
- Multimodal sensing and sensor fusion
- Machine learning for movement prediction, classification, and recommendation
- Task-specific and in-context movement assessment
- Markerless and marker-based motion capture
- Force plate and instrumented treadmill assessment
- Inertial measurement units and wearables
- Movement efficiency and economy
- Injury risk reduction and return to play
- Neuromechanics and motor control
- Sport and health analytics
- Comparative and animal biomechanics