Multi-modal Fall Detection
Multi-Modal Fall Detection is a fall detection framework that integrates data from multiple heterogeneous sensing modalities—such as inertial, visual, audio, or environmental sensors—to improve the accuracy and robustness of fall identification by leveraging complementary information across modalities.
18
Publications
1K
Citations
66
Authors
34
Institutions
Publications per year
2017–2026
10
Parents
18
UP-Fall Detection Dataset: A Multimodal Approach
Lourdes Martínez-Villaseñor, Hiram Pönce, Jorge Brieva et al. · Sensors · 2019 · 386 citations · Full text
1–5 of 18
66
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 66
34

Hanoi University of Science and Technology
Hanoi, Vietnam
2 h-index
9 publications
441 citations

Boğaziçi University
Istanbul, Türkiye
2 h-index
14 publications
175 citations

Villanova University
Radnor, United States
1 h-index
1 publications
57 citations

Marconi University
Rome, Italy
1 h-index
2 publications
128 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
1 publications
64 citations
Université de Versailles Saint-Quentin-en-Yvelines
Versailles, France
1 h-index
1 publications
12 citations
Vietnam Posts and Telecommunications Group (Vietnam)
Ho Chi Minh City, Vietnam
1 h-index
2 publications
98 citations
National Centre of Scientific Research "Demokritos"
Athens, Greece
1 h-index
3 publications
36 citations
1–8 of 34
2 publications · 450 citations
2
h-index
1 publications · 12 citations
1
h-index
Repository · 1 publications · 24 citations
1
h-index
1 publications · 12 citations
1
h-index
1 publications · 67 citations
1
h-index
1–5