Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Analyzing human gait patterns for malfunction detection
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Applied Signal Processing
Gait classification in children with cerebral palsy by Bayesian approach
Pattern Recognition
Gaussian Process Person Identifier Based on Simple Floor Sensors
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
Classification of cerebral palsy gait by Kernel Fisher Discriminant Analysis
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
Human behavior analysis based on a new motion descriptor
IEEE Transactions on Circuits and Systems for Video Technology
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
A multi-classifier for grading knee osteoarthritis using gait analysis
Pattern Recognition Letters
IEEE Transactions on Image Processing
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
Expert Systems with Applications: An International Journal
Combining neural networks for gait classification
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Statistical analysis of gait data to assist clinical decision making
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Clinical gait analysis is an area aimed at the provision of support for diagnoses and therapy considerations, the development of bio-feedback systems to train patients, and the recognition of effects of multiple diseases and still active compensation. The data recorded with ground reaction force measurement platforms is a convenient starting point for gait analysis. The authors argue in favor of using the raw data from such force platforms and apply artificial neural networks for gait malfunction identification. They discuss their latest results in this line of research by using a supervised learning rule. The employed classification approach is learning vector quantization which proved to be highly robust in the training process yielding a remarkably high recognition accuracy of gait patterns.