Welcome to the InfoSci Platform
Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques

Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques

Anupam Shukla, Chandra Prakash Rathore, Neera Bhansali
Copyright: © 2020 |Pages: 21
ISBN13: 9781799804147|ISBN10: 1799804143|EISBN13: 9781799804154
DOI: 10.4018/978-1-7998-0414-7.ch068
Cite Chapter Cite Chapter

MLA

Shukla, Anupam, et al. "Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques." Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1223-1243. https://doi.org/10.4018/978-1-7998-0414-7.ch068

APA

Shukla, A., Rathore, C. P., & Bhansali, N. (2020). Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques. In I. Management Association (Ed.), Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 1223-1243). IGI Global. https://doi.org/10.4018/978-1-7998-0414-7.ch068

Chicago

Shukla, Anupam, Chandra Prakash Rathore, and Neera Bhansali. "Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques." In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1223-1243. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0414-7.ch068

Export Reference

Mendeley
Favorite

Abstract

Parkinson's disease is a degenerative disorder of the central nervous system which occurs as a result of dopamine loss, a chemical mediator that is responsible for body's ability to control the movements. It's a very common disease among elder population effecting approx 6.3 million people worldwide across all genders, races and cultures. In this chapter, authors have proposed an automated classification system based on Artificial Neural Network using Feed Forward Back-propagation Algorithm for Parkinson's disease diagnosis by analyzing gait of a person. The system is trained, tested and validated by a gait dataset consisting data of Parkinson's disease patients and healthy persons. The system is evaluated based on several measuring parameters like sensitivity, specificity, and classification accuracy. For the proposed system observed classification accuracy is 97.11% using 19 features of gait, and 95.55% using 10 prominent features of gait selected by Genetic Algorithm.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.