Author ORCID Identifier

https://orcid.org/0009-0002-2991-7384

Semester

Summer

Date of Graduation

2026

Document Type

Problem/Project Report

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Stephen M. Cain

Committee Member

Nicholas Szczecinski

Committee Member

Natalia Schmid

Abstract

Kathylee Pinnock Branford Manual wheelchairs are essential to mobility and independence, yet accurately characterizing wheelchair motion in real-world environments remains challenging. Inertial measurement units (IMUs) provide a practical means of estimating wheelchair kinematics, but existing trajectory-reconstruction studies have largely been limited to short, controlled trials. This report developed and evaluated a four-IMU framework comprising one sensor on each rear wheel and two sensors on the wheelchair frame. Three orientation algorithms (VQF, Mahony AHRS, and Madgwick AHRS), two accelerometer ranges (±2 g and ± 4 g), and calibrated and uncalibrated sensor data were evaluated during indoor motion-capture trials and a outdoor campus route. Indoor trajectories were most accurately reconstructed using frame-based Madgwick AHRS, with two dimensional position RMSE as low as 0.116 m for the fused estimate in the repeated-turning trial. Outdoors, the ±2 g configuration produced substantially lower trajectory and slope errors than the ±4 g configuration. For the representative calibrated ±2 g trial, ChairL-VQF produced the lowest GPS-referenced two-dimensional position RMSE (8.67 m), while ChairR-VQF produced the lowest full-trial relative loop-closure error (0.29%). Terrain and ramp slope were estimated most accurately using ChairR-Madgwick AHRS, with RMSE values of 1.21° and 2.22°, respectively. Calibration had little effect on the ±2 g results but was essential for the ±4 g configuration, for which uncalibrated data produced implausible trajectory estimates. These findings support the use of calibrated ±2 g sensors, with VQF prioritized for long-duration trajectory reconstruction and Madgwick AHRS prioritized for slope estimation.

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