Comparative Study on Fall Detection using Machine Learning Approaches-Reference-Cited by-同舟云学术

Comparative Study on Fall Detection using Machine Learning Approaches

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
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Author:

Tongskulroongruang Thienrawit¹,Wiphunawat Paphatchaya¹,Jutharee Wisanu²,Kaewmahanin Watchara³,Rassameecharoenchai Teerameth²,Jennawasin Tanagorn¹,Kaewkamnerdpong Boonserm⁴

Affiliation:

1. King Mongkut’s University of Technology Thonburi,Department of Control Systems and Instrumentation Engineering,Bangkok,Thailand

2. Institute of Field Robotics and Automation King Mongkut’s University of Technology Thonburi,Bangkok,Thailand

3. Naresuan University,Faculty of Allied Health Sciences,Department of Cardiothoracic Technology,Phitsanulok,Thailand

4. King Mongkut’s University of Technology Thonburi,Biological Engineering Program Faculty of Engineering,Bangkok,Thailand

Funder

National Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9795349/9795357/09795445.pdf?arnumber=9795445

Reference16 articles.

1. SisFall: A Fall and Movement Dataset

2. Feature Selection Techniques using for High Dimensional Data in Machine Learning;sujatha;International Journal of Engineering Research and Technology,2013

3. Consequences of falling in older men and women and risk factors for health service use and functional decline

4. Fall detection using machine learning algorithms

5. Artificial Neural Networks as an alternative to traditional fall detection methods

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