Human Gait Analysis and Classification Based on Neural Networks and Fuzzy Logic

Jolanta Pauk; Marcin Derlatka; Rafał Długosz; Marta Kolasa

doi:10.4028/www.scientific.net/SSP.147-149.600

Paper Titles

Analysis of Dynamic Method of Line Scales Detection
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Application of Optical Flow and Methods of Group Lee Representation in Problems of Detecting of Movements and Pattern Detection on the Images
p.582

Data Mining in Analysis of Biomechanical Signals
p.588

Decomposition of Non-Stationary Signals Based on the Cochlea Function Principle
p.594

Human Gait Analysis and Classification Based on Neural Networks and Fuzzy Logic
p.600

Parametric Description of Vibration Using the Autocorrelation Function
p.606

Synthesis of a Sequential Model of Muscles of a Man during Drop Landing on a Hard Platform
p.612

Vibration Data Analysis in Presence of Distorting Pulses
p.621

A Fiber-Optic Bending Sensor for the Vibration Monitoring of CNC Face-Milling Machine
p.627

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Human Gait Analysis and Classification Based on Neural Networks and Fuzzy Logic

Abstract:

Human gait analysis and classification is the process of identifying individuals by their walking manners. Computerized gait analysis using neural networks and fuzzy logic has become an integral part of the treatment decision-making process. Authors proposed the integration of kinetic data, more specifically power joints in combination with neural networks and fuzzy logic. It is a relatively new addition to other types of data including temporal and stride parameters. The performance of our approach was verified in laboratory for motion analysis. The obtained results are satisfying.