University of Oulu

Automated gait segmentation and tracking using inertial measurement units

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Author: Irvine, Brian1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Pages: 61
Persistent link:
Language: English
Published: Oulu : B. Irvine, 2020
Publish Date: 2020-12-18
Thesis type: Master's thesis (tech)
Tutor: Seppänen, Tapio
Reviewer: Seppänen, Tapio
Partala, Juha


In this thesis, a methodology is presented to automate the labelling, event detection, segmentation, tracking, and parameter extraction of IMU gait data for sensors placed on the feet and shanks. The algorithms presented were tested using IMU data from three different styles of gait, normal gait, antalgic gait, and limited mobility gait. The algorithms developed were found effective for all of the simulated gait styles without mislabelling or detecting erroneous gait segments. The resultant gait trajectories and parameters were analyzed and were found to accurately depict the differences between each of the different styles of gait.

The methodology presented can be used for the rapid and accurate processing of gait data for multiple styles of gait. This quantification of gait data can enable the collection of IMU gait data on a larger scale. This provides an accessible, low-cost option for out-of-laboratory gait data collection.

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Copyright information: © Brian Irvine, 2020. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.