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Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier
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Author(s): Taro Nakano (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Department of Electrical & Electronic Engineering, Tokushima University, Tokushima, Japan), B.T. Nukala (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA), J. Tsay (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA), Steven Zupancic (Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA), Amanda Rodriguez (Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA), D.Y.C. Lie (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA), J. Lopez (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA)and Tam Q. Nguyen (Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA)
Copyright: 2017
Volume: 5
Issue: 1
Pages: 13
Source title:
International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2017010102
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Abstract
Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.
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