@inproceedings{0d4f72cd98954711a19d92616e0b5e17,
title = "VA algorithm for elderly's falling detection with 2d-pose-estimation",
abstract = "This paper aims to reduce the losses in emergency cases of elderly falling in residential living environments. We design a falling detection system that can determine the human pose-estimation using the TensorFlow APIs to identify the falling of seniors. The proposed specific VA algorithm that considers time, velocity and acceleration factors of human movement, the falling detection system can better analyze the falling and obtain more accurate pose-estimation. To examine the proposed system, the experiments were conducted to testify basic specifications of fallings upon real data traces of human motion records. Results show the acceleration of human movement can relatively affect the classification of actions. the proposed approach achieves an accuracy of 88% on the test data on falling detection.",
keywords = "Fall detection, Health care, Pose-estimation",
author = "Pichayakul Jenpoomjai and Potsawat Wosri and Somchoke Ruengittinun and Hu, {Chih Lin} and Chalothon Chootong",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; null ; Conference date: 06-08-2019 Through 09-08-2019",
year = "2019",
month = aug,
doi = "10.1109/Ubi-Media.2019.00053",
language = "???core.languages.en_GB???",
series = "Proceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "236--240",
booktitle = "Proceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019",
}