HUMAN ACTIVITY RECOGNITION AND FALL DETECTION USING VIDEO AND INERTIAL SENSORS
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Date
2022-05
Authors
Yergaliyev, Zhanggir
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
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.
Description
Keywords
Type of access: Open Access, Research Subject Categories::TECHNOLOGY, Human Activity Recognition, fall detection, video and inertial sensors
Citation
Yergaliyev, Z. (2022). Human activity recognition and fall detection using video and inertial sensors (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan