Author:
Dinh Anh,Shi Yang,Teng Daniel,Ralhan Amitoz,Chen Li,Dal Bello-Haas Vanina,Basran Jenny,Ko Seok-Bum,McCrowsky Carl
Abstract
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Biomedical Engineering,Medicine (miscellaneous),Bioengineering
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