Video-based human fall detection in indoor spaces for health monitoring
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Shojaei Hashemi, Anahita
issued: 2025-04-30T07:00:00Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Remote health monitoring is evolving from a luxury to a necessity in residential buildings and smart homes. As the early detection of fall incidents can prevent mortality and alleviate morbidity, automatic fall detection systems are crucial for remote health monitoring. Providing rich content at an affordable cost, video cameras have become prevalent for fall detection. Most video-based fall detection models use classic machine learning, relying on handcrafted that limit their performance and generalization capability. In this thesis, we propose three novel video-based fall detection methods to solve the mentioned weaknesses, while considering privacy concerns associated with video data. Using deep learning, our models automatically learn the most discriminative features without resorting to definitions and assumptions. The first method is among the early video-based fall detection algorithms that employ deep learning. Consisting of a long short-term memory (LSTM) neural network, it works with depth cameras and uses the three-dimensional (3D) locations of major body joints to address privacy concerns. The second method is designed to operate with RGB cameras, targeting applications that require the identity of the fallen person to provide personalized medical help. It is comprised of a two-dimensional (2D) convolutional neural network (CNN) that extracts features from the frames and feeds them to an LSTM neural network. To address privacy concerns, edge computing is essential for models working with RGB videos to avoid video transmission. In designing our third method, we paid specific attention to this aspect by keeping the computational complexity of the model so low that it can be implemented on cameras built-in boards without losing its real-time performance. Spatial features extracted from the frames by a lightweight, yet powerful 2D CNN are sequentially processed by a transformer. Evaluations prove the competing performance of our models with regards to the st…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84555
note: VIDEO-BASED HUMAN FALL DETECTION IN INDOOR SPACES FOR HEALTH MONITORING by Anahita Shojaei Hashemi A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Anahita Shojaei Hashemi, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Video-based Human Fall Detection in Indoor Spaces for Health Monitoring submitted by Anahita Shojaei Hashemi in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering Examining Committee: Dr. Panos Nasiopoulos, Professor, Department of Electrical and Computer Engineering, UBC Supervisor Dr. James J. Little, Professor, Department of Computer Science, UBC Co-supervisor Dr. Shahriar Mirabbasi, Professor, Department of Electrical and Computer Engineering, UBC Supervisory Committee Member Antony Hodgson, Professor, Department of Mechanical Engineering, UBC University Examiner Michael Van De Panne, Professor, Department of Computer Science, UBC University Examiner Additional Supervisory Committee Members: Dr. Victor Leung, Professor, Department of Electrical and Computer Engineering, UBC Supervisory Committee Member iii Abstract Remote health monitoring is evolving from a luxury to a necessity in residential buildings and smart homes. As the early detection of fall incidents can prevent mortality and alleviate morbidity, automatic fall detection systems are crucial for remote health monitoring. Providing rich content at an affordable cost, video cameras have become prevalent for fall detection. Most video-based fall detection models use classic machine learning, relying on handcrafted that limit their performance and generalization capability. In this thesis, we propose thre…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0431598
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Nasiopoulos, Panos
contributor: Little, James Joseph, 1951-
type: Text
identifier URI: http://hdl.handle.net/2429/84555