HUMAN ACTIVITY RECOGNITION AND FALL DETECTION USING VIDEO AND INERTIAL SENSORS

Yergaliyev, Zhanggir

HUMAN ACTIVITY RECOGNITION AND FALL DETECTION USING VIDEO AND INERTIAL SENSORS

dc.contributor.author	Yergaliyev, Zhanggir
dc.date.accessioned	2022-09-16T05:19:05Z
dc.date.available	2022-09-16T05:19:05Z
dc.date.issued	2022-05
dc.description.abstract	Falls are a crucial problem for elderly people. Early detection of falls may prevent or attenuate possible negative consequences for elderly people. There is a number of scientific articles on the topic of detecting falls using machine learning techniques. While some of them focus on fall detection systems based on scalar body sensors, others apply vision based detection. The goal of this thesis is to try to perform a fusion of inertial sensor based and video sensor based modules to provide a more robust solution, as each method has their own drawbacks in terms of both performance and feasibility.	en_US
dc.identifier.citation	Yergaliyev, Z. (2022). Human activity recognition and fall detection using video and inertial sensors (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan	en_US
dc.identifier.uri	http://nur.nu.edu.kz/handle/123456789/6703
dc.language.iso	en	en_US
dc.publisher	Nazarbayev University School of Engineering and Digital Sciences	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/us/	*
dc.subject	Type of access: Open Access	en_US
dc.subject	Research Subject Categories::TECHNOLOGY	en_US
dc.subject	Human Activity Recognition	en_US
dc.subject	fall detection	en_US
dc.subject	video and inertial sensors	en_US
dc.title	HUMAN ACTIVITY RECOGNITION AND FALL DETECTION USING VIDEO AND INERTIAL SENSORS	en_US
dc.type	Master's thesis	en_US
workflow.import.source	science