Doctor of Philosophy (PHD)
Biomedical Engineering (Engineering)
Date of Defense
First Committee Member
Second Committee Member
Christopher Lee Bennett
Third Committee Member
Fourth Committee Member
Fifth Committee Member
Sixth Committee Member
Clinically oriented Inertial Measurement Unit sensor systems consisting of several sensors tethered to a mobile device are emerging as a powerful tool for continuous monitoring of gait in the community setting. Inertial Measurement Units provide indirect information regarding movement of the lower limbs and require gait parameter estimation algorithms to transform their raw kinematic output into physiologically meaning gait parameters. Mobile IMU applications operate under battery life, sensor number, and bandwidth limitations that constrain the information available to use as inputs. This research investigated whether there is sufficient information in lower limb angular velocity signals acquired at low sampling rates to track human motion. Novel algorithms that use gyroscope data from the shanks and thighs and biomechanical models of gait to determine single and double limb support times, gait cycle time, step length, stride length, gait speed, and knee angle were proposed. Concurrent validity of these algorithms with widely accepted criterion measure systems was assessed through Bland Altman analysis and found to be accurate and precise, demonstrating that clinically relevant gait parameters can be captured outside the constraints of the traditional gait lab using IMUs tethered to mobile systems.
Inertial Measurement Unit; gait; gyroscope; knee angle; step length; gait cycle time
Allseits, Eric, "Development and Validation of Real-Time Methods for Estimating Temporal-Spatial Parameters and Knee Joint Angles during Gait using Inertial Measurement Units" (2017). Open Access Dissertations. 1963.
Available for download on Monday, August 19, 2019