Machine Learning For Fall Detection
Machine Learning for Fall Detection is a computational methodology that employs machine learning algorithms to automatically identify and classify human falls from sensor or video data, distinguished by its focus on modeling temporal-spatial patterns indicative of fall events within the broader domain of activity recognition systems.
528
Publications
25.8K
Citations
1.9K
Authors
687
Institutions
Publications per year
2017–2026
387
Parents
528
UP-Fall Detection Dataset: A Multimodal Approach
Lourdes Martínez-Villaseñor, Hiram Pönce, Jorge Brieva et al. · Sensors · 2019 · 386 citations · Full text
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687

Marche Polytechnic University
Ancona, Italy
9 h-index
42 publications
1K citations

Centre National de la Recherche Scientifique
Paris, France
8 h-index
17 publications
314 citations

Chinese Academy of Sciences
Beijing, China
7 h-index
23 publications
1.4K citations

Universidad de Málaga
Málaga, Spain
7 h-index
22 publications
1.1K citations
Nanchang University
Nanchang, China
7 h-index
33 publications
1.5K citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
7 h-index
28 publications
1.6K citations

King Saud University
Riyadh, Saudi Arabia
6 h-index
13 publications
449 citations

University of Toronto
Toronto, Canada
6 h-index
17 publications
561 citations
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47 publications · 3.4K citations
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29 publications · 1.4K citations
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16 publications · 1.3K citations
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14 publications · 1.7K citations
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11 publications · 444 citations
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