Computer vision techniques for human motion analysis have the potential to significantly improve the monitoring of motor rehabilitation processes. With respect
to traditional marker-based techniques, computer vision offers both portability and
low-cost. This thesis describes methods that have been designed for the analysis of
the motor skills of subjects with Down's syndrome. More specifically, the motion
of interest is weight-shifting; this motion plays an important role in the safety of
locomotory activities, as well as of other daily actions.
From a theoretical viewpoint, the thesis proposes several new concepts for human
motion analysis and describes their algorithmic implementation, as well as their
applicability to the detection and description of several motion primitives.
The thesis introduces the concept of curved bounding box, which is an extension
of the rectangular bounding box that is typically used for detection and tracking
of rigid motion. This concept is successfully applied to the detection of deformable motion, such as arm, knee and upper body motions.
A new technique for identifying subject-representative patterns of motion is also
proposed. This technique is based on Motion History Images, which hold both analytical
and visualization power.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/2834 |
| Date | 02 June 2010 |
| Creators | Svendsen, Jeremy Paul |
| Contributors | Branzan Albu, Alexandra |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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