Fall Detection With a Nonintrusive and First-Person Vision Approach

Wang, Xueyi; Talavera, Estefanía; Karastoyanova, Dimka; Azzopardi, George

doi:10.1109/jsen.2023.3314828

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2024

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Cited by 4 publications

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EGOFALLS: A Visual-Audio Dataset and Benchmark for Fall Detection Using Egocentric Cameras

Wang

2024

Lecture Notes in Computer Science

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EGOFALLS: A Visual-Audio Dataset and Benchmark for Fall Detection Using Egocentric Cameras

Wang

2024

Lecture Notes in Computer Science

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Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

Gaya-Morey,

Manresa-Yee,

Buades-Rubio

2024

Appl Intell

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As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. Graphical abstract

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A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Gharghan,

Hashim

2024

Measurement

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Fall Detection With a Nonintrusive and First-Person Vision Approach

Cited by 4 publications

References 59 publications

EGOFALLS: A Visual-Audio Dataset and Benchmark for Fall Detection Using Egocentric Cameras

EGOFALLS: A Visual-Audio Dataset and Benchmark for Fall Detection Using Egocentric Cameras

Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

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