The study of human motion from a medical standpoint has traditionally involved
the use of marker-based motion tracking systems, as well as other sensory devices.
This equipment is often expensive, has low-portability, and might even influence
tracking results by distracting or otherwise inhibiting a subject's normal motion performance.
In comparison, non-marker-based tracking methods are less costly, easier
to move and set up, and requires no markers or other devices. However, previous work
in silhouette-based human motion analysis is typically focused on the classification of
activities or the identification of subjects, neither of which are much use to medical
professionals.
We propose a merging of these two research fields. By applying silhouette-based
motion tracking to the problem of motion performance analysis, we have developed
a new method which can reliably and accurately model human motions and detect
abnormalities. Our approach, which is based on Hidden Markovian Models with continuous
observation probabilities, creates standardized models to represent common
human motions. These models are then used as a basis for further analysis. We have
extensively tested the proposed method with a custom designed database that takes
into account speed related and subject related variations of motion performance. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3293 |
| Date | 18 May 2011 |
| Creators | Beugeling, Trevor Robert John |
| 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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