This thesis presents the development and implementation of a machine learning prediction model for concurrently aggregating interval linear step distance predictions before future foot placement. Specifically, on-body inertial measurement units consisting of accelerometers, gyroscopes, and magnetometers, through integrated development by Xsens, are used for measuring human walking behavior in real-time. The data collection process involves measuring activity from two subject participants who travel an intended course consisting of flat, stair, and sloped walking elements. This work discusses the formulation of the ensemble machine learning prediction algorithm, real-time application design considerations, feature extraction and selection, and experimental testing under which this system performed several different test case conditions. It was found that the system was able to predict the linear step distances for 47.2% of 1060 steps within 7.6cm accuracy, 67.5% of 1060 steps within 15.2cm accuracy, and 75.8% of 1060 steps within 23cm. For separated flat walking, it was found that 93% of the 1060 steps have less than 25% error, and 75% of the 1060 steps have less than 10% error which is an improvement over the commingled data set. Future applications and work to expand upon from this system are discussed for improving the results discovered from this work. / Master of Science / This thesis presents the development and implementation of a machine learning prediction model for determining the stepping distance of future steps in real-time walking before their placement occurs. Specialized sensor units for measuring human motion activity are worn on the body for collecting and characterizing human walking behavior in real-time. Two subject participants are asked to walk a planned course consisting of flat, stair, and sloped walking elements. This work discusses the prediction algorithm voting scheme, real-time application design considerations, descriptive data elements for the algorithm, and experimental testing under which this system performed several different test case conditions. Detailed experimental tests are concluded in order to fully understand the extent of the system’s performance and the behaviors it exhibits throughout. The approach explored in this work enables researchers and roboticists to develop improvements and construct variations which may become superior to this method.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/82329 |
| Date | 22 February 2018 |
| Creators | Barrows, Bryan Alan |
| Contributors | Mechanical Engineering, Asbeck, Alan T., Wicks, Alfred L., Furukawa, Tomonari |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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