Date of Award

Summer 2018

Document Type


Degree Name

Master of Science (MS)


Computer Science

Committee Director

Tamer Nadeem

Committee Member

Ravi Mukkamala

Committee Member

Michele Weigle


Analysis of the human gait is used in many applications such as medicine, sports, and person identification. Several research studies focused on the use of MEMS inertial sensors for gait analysis and showed promising results. The miniaturization of these sensors and their wearability allowed the analysis of gait on a long term outside of the laboratory environment which can reveal more information about the person and introduced the use of gait analysis in new applications such as indoor localization.

Step detection and step length estimation are two basic and important gait analysis tasks. In fact, step detection is a prerequisite for the exploration of all other gait parameters. Researchers have proposed many methods for step detection, and their experiments results showed high accuracies that exceeded 99% in some cases. All of these methods rely on experimental thresholds selected based on a limited number of subjects and walking conditions. Selecting and verifying an optimal threshold is a difficult task since it can vary according to a lot of factors such as user, footwear, and the walking surface material. Also, most of these methods do not distinguish walking from other activities; they can only recognize motion state from idle state. Methods that can be used to distinguish walking from other activities are mainly machine learning methods that need training and complex data labeling. On the other hand, step length estimation methods used in the literature either need constant calibration for each user, rely on impractical sensor placement, or both.

In this thesis, we employ the human walking bipedal nature for gait analysis using two MEMS gyroscopes, one attached to each side of the lower waist. This setup allowed the step detection and discrimination from other non bipedal activities without the need for magnitude thresholds or training. We were also able to calculate the hip rotation angle in the sagittal plane which allowed us to estimate the step length. without needing for constants calibration. By mounting an accelerometer on the center of the back of the waist, we were able to develop a method to auto-calibrate the Weinberg method constant, which is one of the most accurate step length estimation methods, and increase its accuracy even more.


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