Bradford Multi-Modal Gait Database: Gateway to Using Static Measurements to Create a Dynamic Gait Signature
| Autor: | Alawar, Hamad M.M.A., Ugail, Hassan, Kamala, Mumtaz A., Connah, David |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Druh dokumentu: | Článek |
| DOI: | 10.9734/BJAST/2016/13426 |
| Popis: | Yes Aims: To create a gait database with optimum accuracy of joint rotational data and an accu-rate representation of 3D volume, and explore the potential of using the database in studying the relationship between static and dynamic features of a human’s gait. Study Design: The study collected gait samples from 38 subjects, in which they were asked to walk, run, walk to run transition, and walk with a bag. The motion capture, video, and 3d measurement data extracted was used to analyse and build a correlation between features. Place and Duration of Study: The study was conducted in the University of Bradford. With the ethical approval from the University, 38 subjects’ motion and body volumes were recorded at the motion capture studio from May 2011- February 2013. Methodology: To date, the database includes 38 subjects (5 females, 33 males) conducting walk cycles with speed and load as covariants. A correlation analysis was conducted to ex-plore the potential of using the database to study the relationship between static and dynamic features. The volumes and surface area of body segments were used as static features. Phased-weighted magnitudes extracted through a Fourier transform of the rotation temporal data of the joints from the motion capture were used as dynamic features. The Pearson correlation coefficient is used to evaluate the relationship between the two sets of data. Results: A new database was created with 38 subjects conducting four forms of gait (walk, run, walk to run, and walking with a hand bag). Each subject recording included a total of 8 samples of each form of gait, and a 3D point cloud (representing the 3D volume of the subject). Using a Pvalue (P |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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