TY - GEN
T1 - A real-time robust fall detection system using a wireless gait analysis sensor and an Artificial Neural Network
AU - Nukala, B. T.
AU - Shibuya, N.
AU - Rodriguez, A. I.
AU - Tsay, J.
AU - Nguyen, T. Q.
AU - Zupancic, S.
AU - Lie, D. Y.C.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/10
Y1 - 2014/2/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84949922022&partnerID=8YFLogxK
U2 - 10.1109/HIC.2014.7038914
DO - 10.1109/HIC.2014.7038914
M3 - Conference contribution
AN - SCOPUS:84949922022
T3 - 2014 IEEE Healthcare Innovation Conference, HIC 2014
SP - 219
EP - 222
BT - 2014 IEEE Healthcare Innovation Conference, HIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Healthcare Innovation Conference, HIC 2014
Y2 - 8 October 2014 through 10 October 2014
ER -