A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier

N. Shibuya, B. T. Nukala, A. I. Rodriguez, J. Tsay, T. Q. Nguyen, S. Zupancic, D. Y.C. Lie

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

35 Scopus citations

Abstract

In this study, we report a custom designed wireless gait analysis sensor (WGAS) system for real-time fall detection using a Support Vector Machine (SVM) classifier. Our WGAS includes a tri-axial accelerometer, 2 gyroscopes and a MSP430 micro-controller. It was worn by the subjects at either the T4 or at the waist level for various intentional falls, Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) test. The raw data of tri-axial acceleration and angular velocity is wirelessly transmitted from the WGAS to a nearby PC, and then 6 features were extracted for fall classification using a SVM (Support Vector Machine) classifier. We achieved 98.8% and 98.7% fall classification accuracies from the data at the T4 and belt positions, respectively. Moreover, the preliminary data demonstrates an impressive overall specificity of 99.5% and an overall sensitivity of 97.0% for this WGAS real-time fall detection system.

Original languageEnglish
Title of host publication2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-67
Number of pages2
ISBN (Electronic)9784907626129
DOIs
StatePublished - Mar 13 2015
Event2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015 - Hakodate, Japan
Duration: Jan 20 2015Jan 22 2015

Publication series

Name2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015

Conference

Conference2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015
CountryJapan
CityHakodate
Period01/20/1501/22/15

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  • Cite this

    Shibuya, N., Nukala, B. T., Rodriguez, A. I., Tsay, J., Nguyen, T. Q., Zupancic, S., & Lie, D. Y. C. (2015). A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier. In 2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015 (pp. 66-67). [7061032] (2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMU.2015.7061032