Sensor fusion for enhancing motion capture : integrating optical and inertial motion capture systems
Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Industrial Engineering
Committee Chair
Howard Chen
Committee Member
Sara Harper
Committee Member
Bryan Mesmer
Research Advisor
Howard Chen
Subject(s)
Human locomotion--Measurement, Human locomotion--Computer simulation, Biomechanics, Human engineering
Abstract
This study aimed to create and evaluate a custom sensor fusion algorithm based on Gauss-Newton optimization that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to combat the limitations of solely using each system. The goal was to demonstrate how inertial measurement unit (IMU) data can be seamlessly integrated into motion capture data to provide a more efficient and reliable gap filling process for future research. The algorithm takes the first and last frame of OMC data, as quaternions, and fills the rest with IMU gyroscope orientation data to simulate gaps of up to ten minutes. The algorithm presented average total errors of < 2.0° across a 5-minute duration for all three sensor placements. The results demonstrated that the fusion of these two sensing modalities is feasible and shines light on the possibility of more field-based studies for human motion analysis.
Recommended Citation
Hicks, Hailey, "Sensor fusion for enhancing motion capture : integrating optical and inertial motion capture systems" (2025). Theses. 747.
https://louis.uah.edu/uah-theses/747