Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Human motion analysis: a review
Computer Vision and Image Understanding
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Nonparametric Recognition of Nonrigid Motion
Nonparametric Recognition of Nonrigid Motion
The Perception of Articulated Motion: Recognizing Moving Light Displays
The Perception of Articulated Motion: Recognizing Moving Light Displays
Gait analysis for classification
Gait analysis for classification
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
New developments in self-organizing systems
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Gender classification in human gait using support vector machine
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Probabilistic Self-Organizing Graphs
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Multivariate Student-t self-organizing maps
Neural Networks
Efficient human action and gait analysis using multiresolution motion energy histogram
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
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This research addresses the question of the existence of prominent diagnostic signatures for human walking extracted from kinematics gait data. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.