TY - GEN
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 - Zupancic, Steven
AU - Rodriguez, Amanda
AU - Lie, D. Y.C.
AU - Lopez, J.
AU - Nguyen, Tam Q.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/23
Y1 - 2016/8/23
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, we 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, we 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, we 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, we 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, we 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, we 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=84988014431&partnerID=8YFLogxK
U2 - 10.1109/ICIS.2016.7550922
DO - 10.1109/ICIS.2016.7550922
M3 - Conference contribution
AN - SCOPUS:84988014431
T3 - 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
BT - 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
A2 - Uehara, Kuniaki
A2 - Nakamura, Masahide
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
Y2 - 26 June 2016 through 29 June 2016
ER -