Author

Hailey Hicks

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.

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