@inproceedings{9f7a9615cda64b93864e760c8bef52c4,
title = "A machine learning approach to fall detection algorithm using wearable sensor",
abstract = "Falls are the primary cause of accidents for the elderly in living environment. Falls frequently cause fatal and non-fatal injuries that are associated with a large amount of medical costs. Reduction hazards in living environment and doing exercise for training balance and muscle are the common strategies for fall prevention. But falls cannot be avoided completely; fall detection provides the alarm in time that can decrease the injuries or death caused by no rescue. We propose machine learning-based fall detection algorithm using multi-SVM with linear, quadratic or polynomial kernel function, and k-NN classifier. Eight kinds of falling postures and seven types of daily activities arranged in the experiment are used to explore the performance of the machine learning-based fall detection algorithm. The emulated falls were performed on a soft mat by ten healthy young subjects wearing protectors. The k-nearest neighbor method with 0.1 second window size has the highest accuracy, which is 96.26%. The results show that the proposed machine learning fall detection algorithm can fulfill the requirements of adaptability and flexibility for the individual differences.",
keywords = "fall detection, machine learning, wearable sensor",
author = "Hsieh, {Chia Yeh} and Huang, {Chih Ning} and Liu, {Kai Chun} and Chu, {Woei Chyn} and Chan, {Chia Tai}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Advanced Materials for Science and Engineering, IEEE-ICAMSE 2016 ; Conference date: 12-11-2016 Through 13-11-2016",
year = "2017",
month = feb,
day = "2",
doi = "10.1109/ICAMSE.2016.7840209",
language = "English",
series = "Proceedings of the IEEE International Conference on Advanced Materials for Science and Engineering: Innovation, Science and Engineering, IEEE-ICAMSE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "707--710",
editor = "Teen-Hang Meen and Prior, {Stephen D.} and Lam, {Artde Donald Kin-Tak}",
booktitle = "Proceedings of the IEEE International Conference on Advanced Materials for Science and Engineering",
address = "United States",
}