Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier

Taro Nakano, B. T. Nukala, Steven Zupancic, Amanda Rodriguez, D. Y.C. Lie, J. Lopez, Tam Q. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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, 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.

Original languageEnglish
Title of host publication2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
EditorsKuniaki Uehara, Masahide Nakamura
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008063
DOIs
StatePublished - Aug 23 2016
Event15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan
Duration: Jun 26 2016Jun 29 2016

Publication series

Name2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

Conference

Conference15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
CountryJapan
CityOkayama
Period06/26/1606/29/16

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Keywords

  • Balance disorders
  • Fall risks
  • Gait analysis
  • Support Vector Machine (SVM)
  • Wearable gait sensor
  • Wireless Gait Sensor

Cite this

Nakano, T., Nukala, B. T., Zupancic, S., Rodriguez, A., Lie, D. Y. C., Lopez, J., & Nguyen, T. Q. (2016). Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier. In K. Uehara, & M. Nakamura (Eds.), 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings [7550922] (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIS.2016.7550922