Real -time Fall Detection and Alert System Using Pose Estimation

Safarzadeh, Meysam; Alborzi, Yusef; Ardekany, Ali Naiafi

doi:10.1109/icrom48714.2019.9071856

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…A significant work for fall detection using MLP was proposed by Safarzadeh et al (2019) [130]. They introduced a two-stage real-time fall detection system.…”

Section: Mlp Based Techniquesmentioning

confidence: 99%

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-thearts non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.

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“…A significant work for fall detection using MLP was proposed by Safarzadeh et al (2019) [130]. They introduced a two-stage real-time fall detection system.…”

Section: Mlp Based Techniquesmentioning

confidence: 99%

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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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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