Research

Traditional marker-based motion capture is considered to be a gold standard for analyzing human movement and has been used to study biomechanics of activities of daily living (ADL). But it is time-consuming, requires specially trained researchers to place markers on anatomical lanandmarks and participants to wear specific clothes.  Markerless motion capture is relatively new motion capture method that uses machine learning to identify key points in videos of study participants. It is faster and it can be done outside of traditional lab settings, which makes it more convenient and accessible for participants.

Markerless motion capture has not yet been validated for certain ADLs including step-up and step-down movements which simulate everyday activity of climbing stairs. If markerless motion capture is validated for this movement, it could lead to faster collection of data and a larger sample size of a more diverse population which is important for drawing more conclusions. That data then be used to analyze how climbing up and down the stairs could affect the progression of certain diseases including knee osteoarthiritis.