Research on Classification of Gait Signal Based on Neural Network

Abstract:

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP), and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1081-1084
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1081
• Online since: February 2013
• Authors: Jing Yin
• Keywords: BP Neural Network (BPNN), Coefficient of Variation, Feature Extraction, Parkinson’s Disease

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