FURI | Spring 2024

Sensor Fusion System for Improving Motion Amount Quantification within Computer-Vision-Enabled Worker Analysis

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In general, machine learning models are as accurate as the datasets that they are fed; in the context of computer vision, this accuracy is determined by sensor quality, input variability, and environmental stability. This project aims to reduce the impact of the aforementioned variables on computer vision models by developing a sensor fusion system to consolidate inputs from multiple sensors. This would decrease costs and increase accuracy, making it feasible for computer vision technology to be applied to existing workplaces.

Student researcher

Ethan Chang

Computer systems engineering

Hometown: Novato, California, United States

Graduation date: Spring 2025