Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5

Ke, Yaojie; Yao, Yinan; Xie, Zhengye; Xie, Hepeng; Lin, Hui; Dong, Chen

doi:10.1109/dasc/picom/cbdcom/cy59711.2023.10361490

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf 2023

DOI: 10.1109/dasc/picom/cbdcom/cy59711.2023.10361490

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Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5

Yaojie Ke,

Yinan Yao,

Zhengye Xie

et al.

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2024

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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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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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Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5

Cited by 1 publication

References 24 publications

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

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

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