Name
Abstract | ||
|---|---|---|
Human fall detection is a problem that needs to be addressed to decrease the significant number of elderly people being affected, disabled, or even killed by falls. While the prevention of falls is a goal harder, or impossible, to be achieved, the fast detection and aid of people are two aspects that technological solutions can help with. With the support of internet of things devices, a fall detection solution for building deployment is proposed in this paper. The classification of fall is done using the Morlet wavelet in the fog-computing layer, enabling the detection of falls near the person and near the people who can provide first aid. The proposed solution of this paper was tested using a new dataset created using a human-body model. The results are promising, proving the efficiency of the proposed solution. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-78901-5_2 | SUSTAINABLE SMART CITIES AND TERRITORIES |
Keywords | DocType | Volume |
Internet of Things, Fall detection, Wavelet transform | Conference | 253 |
ISSN | Citations | PageRank |
2367-3370 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osvaldo Ribeiro | 1 | 0 | 0.34 |
| Luis Gomes | 2 | 0 | 0.68 |
| Zita A. Vale | 3 | 0 | 0.34 |