A real-time robust fall detection system using a wireless gait analysis sensor and an Artificial Neural Network

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

This paper describes our custom-designed wireless gait analysis sensor (WGAS) system developed and tested for real-time fall detection. The WGAS is capable of differentiating falls vs. Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) performed by young volunteers using a Back Propagation Artificial Neural Network (BP ANN) algorithm. The WGAS, which includes a tri-Axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller is worn by the subjects at either T4 (at back) or the belt-clip positions (in front of the waist) for the various falls, ADL, and Dynamic Gait Index (DGI) tests. The raw data is wirelessly transmitted from the WGAS to a nearby PC for real-time fall classification, where six features were extracted for the BP ANN. Overall fall classification accuracies of 97.0% and 97.4% have been achieved for the data taken at the T4 and at the belt position, respectively. The preliminary data demonstrates an overall sensitivity of 97.0% and overall specificity of 97.2% for this WGAS fall detection system, showing good promise as a real-time low-cost and effective fall detection device for wireless acute care and wireless assisted living.

Original languageEnglish
Title of host publication2014 IEEE Healthcare Innovation Conference, HIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-222
Number of pages4
ISBN (Electronic)9781467363648
DOIs
StatePublished - Feb 10 2014
Event2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States
Duration: Oct 8 2014Oct 10 2014

Publication series

Name2014 IEEE Healthcare Innovation Conference, HIC 2014

Conference

Conference2014 IEEE Healthcare Innovation Conference, HIC 2014
Country/TerritoryUnited States
CitySeattle
Period10/8/1410/10/14

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