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Janah Emeeshat's Ph.D. Dissertation.pdf (2.08 MB)

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An Obstacle Detection and Fall Prevention System for Elderly People

Emeeshat, Janah Salama
http://rave.ohiolink.edu/etdc/view?acc_num=case16491629639297

Abstract Details

2022, Doctor of Philosophy, Case Western Reserve University, EECS - Electrical Engineering.
Obstacle detection and warning can help elderly people enhance their mobility as well as their safety, especially in enclosed spaces (indoor environments). As people age, falling poses a significant risk, therefore providing mechanisms to prevent falls is vital to improve the safety and wellness of the elderly people population. Every year, millions of individuals in the United States are treated in emergency departments for fall-related injuries, which result in fractures, loss of independence, and even death. As a result, this issue must be addressed promptly. Fall prevention has been a focus of research for more than a decade, to enhance people's lives through the use of technology. This is primarily motivated by the impact that falls have in terms of mortality, morbidity, and social expense, which puts them on par with road traffic injuries in terms of mortality, morbidity, and social costs. Falls detection for elderly people can be essential to diminish the mortality rate and limit the associated health impacts. Technological solutions designed to automatically detect and inform a fall may be categorized into wearable and non-wearable solutions. Fall prevention systems take advantage of external sensors and wearable sensors where different motion characteristics are extracted from the collected data and are used to estimate the likelihood of a fall and alert the user in real-time. This work proposes an obstacle detection system to inhibit falls in the indoor environment. When obstacles are detected, the system will provide alarm messages to grab the user’s attention. Because the elderly people spend a lot of their time at home, the proposed detection system is designed mainly for indoor applications. For this, firstly, obstacles are detected and localized, and then the information about the obstacles will be sent to the walker using an audio alert. In this dissertation, we present an assistive system for elderly people based on a network of sensors that includes two main components: environment information acquisition and analysis and information representation. The first component focuses on detecting the environment by using ultrasonic sensors and IMUs and analyzing their data to detect the obstacles for elderly people, while the second component tries to depict the obstacle’s information in the form of acoustic and written feedback. The dissertation also provides a simulation environment using Gazebo and ROS that provides a framework for the design, implementation, simulation, and testing of the proposed wearable obstacle detection and fall prevention system. The intention behind this approach is to enhance the walker’s awareness and his/her disposition to be careful. Environmental fall prevention systems endeavor to collect enough data from the walker’s milieu to recognize and avoid fall hazards in real-time. They also alert the walker of potential tripping obstacles in his/her walkways. Over the years, the main focus has been on fall detection, as opposed to fall prevention, as evidenced by the limited number of articles that are available in the existing literature.
Dr. Kenneth A. Loparo (Committee Chair)
Dr. Wyatt Newman (Committee Member)
Dr. Farhad Kaffashi (Committee Member)
Dr. Michael Fu (Committee Member)
135 p.
Electrical Engineering
Falls, Obstacle detection, Fall prevention, Elderly people

Recommended Citations

Citations

  • Emeeshat, J. S. (2022). An Obstacle Detection and Fall Prevention System for Elderly People [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case16491629639297

    APA Style (7th edition)

  • Emeeshat, Janah. An Obstacle Detection and Fall Prevention System for Elderly People. 2022. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case16491629639297.

    MLA Style (8th edition)

  • Emeeshat, Janah. "An Obstacle Detection and Fall Prevention System for Elderly People." Doctoral dissertation, Case Western Reserve University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=case16491629639297

    Chicago Manual of Style (17th edition)

case16491629639297
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.
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