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Virginia Tech

Joint Angle Estimation Method for Wearable Human Motion Capture

Abstract

dc:description.abstract

This thesis presents a method for estimating the positions of human limbs during motion that can be applied to wearable, textile-based sensors. The method was validated for the elbow and shoulder joints with data from two garments with resistive, thread-based sensors sewn into the garments at multiple locations. The proposed method was able to estimate the elbow joint position with an average error of 2.2 degrees. The method also produced an average difference in Euclidean distance of 3.7 degrees for the estimated shoulder joint position using data from nine sensors placed around the subject's shoulder. The most accurate combination of sensors on the shoulder garment was found to produce an average difference in distance of 3.4 degrees and used only six sensors. The characteristics of the resistive, thread-based sensor used to validate the method are also detailed as some of their behaviors proved to affect the accuracy of the method negatively.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Engineering
Department dc:contributor.department
Electrical and Computer Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Redhouse, Amanda Jean
Chairs dc:contributor.committeechair
  • Martin, Thomas L.
  • Jones, Mark T.
Committee member dc:contributor.committeemember
  • Abbott, A. Lynn

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:31129
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/103629

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Redhouse, Amanda Jean. Joint Angle Estimation Method for Wearable Human Motion Capture. masters thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103629