Clinical Motion Lab in Your Pocket
Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. We developed AI-based algorithms for quantifying gait pathology using commodity cameras. Our methods increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct studies of neurological and musculoskeletal disorders at an unprecedented scale.
3 Key Takeaways:
*Quantitative assessment of movement enables diagnostics and treatment of many neurological disorders
*Existing methods for quantitative analysis of movement require very expensive equipment
*Deep learning models can predict common gait metrics using a mobile phone camera
Łukasz Kidziński is a co-founder of Saliency and a research associate in the Neuromuscular Biomechanics Lab at Stanford University, applying state-of-the-art computer vision and reinforcement learning algorithms for improving clinical decisions and treatments. Previously he was a researcher in the CHILI group, Computer-Human Interaction in Learning and Instruction, at the EPFL in Switzerland, where he was developing methods for measuring and improving engagement of users in massive online open courses. He obtained a Ph.D. degree at Université Libre de Bruxelles in mathematical statistics, working on frequency-domain methods for dimensionality reduction in time series.