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On Automated Model-Based Extraction and Analysis of Gait

On Automated Model-Based Extraction and Analysis of Gait
On Automated Model-Based Extraction and Analysis of Gait
We develop a new model-based extraction process guided by biomechanical analysis for walking people, and analyse its data for recognition capability. Hierarchies of shape and motion yield relatively modest computational demands, while anatomical data is used to generate shape models consistent with normal human body proportions. Mean gait data is used to create prototype gait motion models, which are adapted to fit individual subjects. Our approach is evaluated on a large gait database, comprising 4824 sequences from 115 subjects, demonstrating gait extraction and description capability in laboratory and real-world capture conditions. Recognition capability is illustrated by an 84% CCR in laboratory conditions, which is reduced for real-world (outdoor) data. Preliminary results from a statistical analysis of the extracted gait parameters, suggest that recognition capability is primarily gained from cadence and from static shape parameters, although gait is the cue by which these are derived.
gait, walking, model-based
11-16
Wagg, David K
a6df8725-c301-4516-8cfc-e63afacfb166
Nixon, Mark S
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Azada, Deeber
2ff59fed-f626-4525-bb39-7771375bd7c9
Wagg, David K
a6df8725-c301-4516-8cfc-e63afacfb166
Nixon, Mark S
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Azada, Deeber
2ff59fed-f626-4525-bb39-7771375bd7c9

Wagg, David K and Nixon, Mark S (2004) On Automated Model-Based Extraction and Analysis of Gait. Azada, Deeber (ed.) 6th International Conference on Automatic Face and Gesture Recognition. 17 - 19 May 2004. pp. 11-16 .

Record type: Conference or Workshop Item (Paper)

Abstract

We develop a new model-based extraction process guided by biomechanical analysis for walking people, and analyse its data for recognition capability. Hierarchies of shape and motion yield relatively modest computational demands, while anatomical data is used to generate shape models consistent with normal human body proportions. Mean gait data is used to create prototype gait motion models, which are adapted to fit individual subjects. Our approach is evaluated on a large gait database, comprising 4824 sequences from 115 subjects, demonstrating gait extraction and description capability in laboratory and real-world capture conditions. Recognition capability is illustrated by an 84% CCR in laboratory conditions, which is reduced for real-world (outdoor) data. Preliminary results from a statistical analysis of the extracted gait parameters, suggest that recognition capability is primarily gained from cadence and from static shape parameters, although gait is the cue by which these are derived.

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More information

Published date: 2004
Additional Information: Event Dates: 17-19 May, 2004
Venue - Dates: 6th International Conference on Automatic Face and Gesture Recognition, 2004-05-17 - 2004-05-19
Keywords: gait, walking, model-based
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259374
URI: https://eprints.soton.ac.uk/id/eprint/259374
PURE UUID: 7322aba0-6052-4fd4-a487-a94bd7d519e0
ORCID for Mark S Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 24 May 2004
Last modified: 10 Sep 2019 00:57

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Contributors

Author: David K Wagg
Author: Mark S Nixon ORCID iD
Editor: Deeber Azada

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