TY - JOUR
T1 - Gaits classification of Normal vs. Patients by wireless gait sensor and Support Vector Machine (SVM) classifier
AU - Nakano, Taro
AU - Nukala, B. T.
AU - Tsay, J.
AU - Zupancic, Steven
AU - Rodriguez, Amanda
AU - Lie, D. Y.C.
AU - Lopez, J.
AU - Nguyen, Tam Q.
N1 - Publisher Copyright:
Copyright © 2017, IGI Global.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Balance Disorders
KW - Fall Risks
KW - Gait Analysis
KW - Support Vector Machine (SVM)
KW - Wearable Gait Sensor
KW - Wireless Gait Sensor
UR - http://www.scopus.com/inward/record.url?scp=85024088696&partnerID=8YFLogxK
U2 - 10.4018/IJSI.2017010102
DO - 10.4018/IJSI.2017010102
M3 - Article
AN - SCOPUS:85024088696
SN - 2166-7160
VL - 5
SP - 17
EP - 29
JO - International Journal of Software Innovation
JF - International Journal of Software Innovation
IS - 1
ER -