Skeleton-based Fall Detection
Skeleton-Based Fall Detection is a fall detection method that utilizes human skeletal joint coordinates derived from depth or RGB video as the primary input, distinguishing itself from appearance-based or device-centric approaches by focusing exclusively on articulated body pose dynamics for identifying fall events.
49
Publications
2.3K
Citations
146
Authors
73
Institutions
Publications per year
2017–2026
40
Parents
49
Fall Detection Based on Key Points of Human-Skeleton Using OpenPose
Weiming Chen, Zijie Jiang, Hailin Guo et al. · Symmetry · 2020 · 229 citations · Full text
Fall Detection and Activity Recognition Using Human Skeleton Features
Heilym Ramirez, Sergio A. Velastín, Ignacio Meza et al. · IEEE Access · 2021 · 170 citations · Full text
A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
Chuan-Bi Lin, Ziqian Dong, Wei-Kai Kuan et al. · Applied Sciences · 2020 · 122 citations · Full text
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Nanchang University
Nanchang, China
3 h-index
12 publications
495 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
7 publications
1.1K citations
Kingston University
London, United Kingdom
2 h-index
2 publications
183 citations
Pontificia Universidad Católica de Valparaíso
Valparaíso, Chile
2 h-index
6 publications
549 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
2 publications
75 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
2 publications
75 citations
National Taiwan University of Science and Technology
Taipei, Taiwan
2 h-index
6 publications
186 citations

Shaanxi Normal University
Xi'an, China
2 h-index
2 publications
75 citations
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4 publications · 284 citations
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4 publications · 82 citations
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3 publications · 191 citations
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1 publications · 19 citations
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1 publications · 45 citations
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