A Fall and Near-Fall Assessment and Evaluation System
Anh Dinh1, Yang Shi2, Daniel Teng*, 1, Amitoz Ralhan1, Li Chen1, Vanina Dal Bello-Haas3, Jenny Basran4, Seok-Bum Ko1, Carl McCrowsky1
1 Department of Electrical and Computer Engineering, University of Saskatchewan, Canada
2 Department of Mechanical Engineering, University of Saskatchewan, Canada
3 School of Physical Therapy, University of Saskatchewan, Canada
4 Division of Geriatric Medicine, University of Saskatchewan, Canada
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.
Keywords: Fall detection, near-fall data collection, wearable device, machine learning, fall classification, wireless communications..
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
* Address correspondence to this author at the University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N5A9, Canada; Tel: (306) 966-2889; Fax: (306) 966-5407; E-mail: firstname.lastname@example.org