A fall and near-fall assessment and evaluation system.
The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.
Dinh
Anh
A
Department of Electrical and Computer Engineering, University of Saskatchewan, Canada.
Shi
Yang
Y
Teng
Daniel
D
Ralhan
Amitoz
A
Chen
Li
L
Dal Bello-Haas
Vanina
V
Basran
Jenny
J
Ko
Seok-Bum
SB
McCrowsky
Carl
C