Document Type
Dissertation
Date of Award
2025
Degree Name
Doctor of Philosophy (PhD)
Department
Biomedical Engineering
First Advisor
Lisa MacFadden
Abstract
Quantifying human movement through biomechanical assessments provides metrics for evaluating rehabilitation progress and assessing the functional movements of patients in healthcare settings. Traditional tools used for this assessment, including motion capture and wearable sensors, provide accurate results, but have significant barriers that limit their use. The largest barriers to adopting motion capture tools are cost, space requirements, and time it takes to perform these assessments. Recent advances in markerless motion capture (MMC) methods have aimed to reduce one or two of these barriers but have yet to address all three simultaneously. OpenCap is a low-cost MMC tool that uses iPads and provides biomechanical analysis using cloud computing to offer researchers a tool that reduces the time and cost of performing a motion study. Despite advances like OpenCap, there is yet to be a clinical movement assessment tool that can fit into the small spaces present in existing healthcare settings. This work presents CloseCap, a novel MMC tool that uses low-cost cameras and edge computing to perform motion capture in spaces as small as 4 square meters. CloseCap uses state-of-the-art human pose estimation algorithms paired with triangulation methods optimized for wide-angle cameras. The performance of the CloseCap system was assessed against OpenCap to measure its accuracy in performing a biomechanical movement assessment. The comparison between methods was made using simultaneously recorded sessions of a person walking on a treadmill at a steady pace. Thirty walking trials were collected for two different participants. Kinematics for both methods were computed using the OpenSim inverse kinematic pipeline, and root-mean-squared-error for three lower-limb joint angles was calculated and compared. Statistical parametric mapping was also used on normalized gait cycle data to compare the difference in each method’s ability to perform a gait assessment. The study revealed a difference in the joint angles of six to eight degrees depending on the joint measured. These differences are higher than the commonly expected difference of five degrees when comparing kinematic data from different sources. Despite the higher-than-expected difference, CloseCap demonstrated that clinical movement assessments can be performed in spaces much smaller than previously possible.
Subject Categories
Biomechanics and Biotransport
Keywords
Biomechanical assessment, Markerless motion capture (MMC), OpenCap, CloseCap, Edge computing, Human pose estimation, Inverse kinematics, Gait analysis
Number of Pages
124
Publisher
University of South Dakota
Recommended Citation
Rykhus, Ryan, "MARKERLESS MOTION CAPTURE HARDWARE FOR INTEGRATION INTO COMPACT SPACES FOR CLINICAL MOVEMENT ASSESSMENTS" (2025). Dissertations and Theses. 373.
https://red.library.usd.edu/diss-thesis/373