Term of Award
Master of Science, Kinesiology - Exercise Science Concentration
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
Digital Commons@Georgia Southern License
Department of Health and Kinesiology
Committee Member 1
Committee Member 2
Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research examined ML systems’ estimation of the lower extremity joint moments and powers. Objectives: This study primarily aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems during treadmill running. The secondary purpose was to investigate if movement’s speed would affect the ML’s performance. Methods: Sixteen volunteers ran on a treadmill for 120 s for each trial at the speed of 2.24, 2.91, and 3.58 m/s, respectively. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Compared to the MB system, the ML system estimated greater increased hip and knee joint kinetics with faster speeds during the swing phase. Additionally, increased greater ankle joint moments with speed estimated by the ML system were observed at the early swing phase. In contrast, the greater ankle joint powers occurred at the initial stance phase. Conclusions: These observations indicated that inconsistent segment pose estimations (mainly the center of mass estimated by ML was farther away from the relevant distal joint center) might lead to systematic differences in joint moments and powers estimated by MB and ML systems. Despite the promising applications of the ML system in clinical settings, systematic ML overestimation requires extra attention.
Tang, Hui, "Comparison Marker-Based and Markerless Motion Capture Systems in Gait Biomechanics During Running" (2023). Electronic Theses and Dissertations. 2611.
Research Data and Supplementary Material