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Role of Machine Learning for Gait and Reflex Response Classification

  • Robert LeMoyneEmail author
  • Timothy Mastroianni
Chapter
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 27)

Abstract

Over the span of the past decade machine learning has been applied to distinguishing between disparate health status scenarios with considerable classification accuracy. Recent examples pertain to notable classification accuracy with regards to gait and reflex response disparity, especially in the context of a hemiplegic affected leg and unaffected leg. Machine learning classification serves as an instrumental post-processing methodology for the signal acquired through a wearable and wireless accelerometer or gyroscope. A summary of machine learning platforms is presented. The application and demonstration of machine learning as a diagnostic tool is described within the scope of gait, reflex response, and associated subjects. The amalgamation of machine learning and wearable and wireless systems is anticipated to further evolve Network Centric Therapy with capabilities, such as prognostic assessment of rehabilitation, objective consideration of therapy efficacy, therapy optimization, and diagnosis of appropriate transitional phases of therapy strategy.

Keywords

Machine learning Waikato Environment for Knowledge Analysis (WEKA)  J48 decision tree K-nearest neighbors Logistic regression Support vector machine Multilayer perceptron neural network Wireless accelerometer Wireless gyroscope Smartphone Portable media device 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Biological Sciences, Center for Bioengineering InnovationNorthern Arizona UniversityFlagstaffUSA
  2. 2.IndependentPittsburghUSA

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