Name
Abstract | ||
|---|---|---|
Two popular mounting positions of Doppler radar for human fall detection are in the ceiling center and at the torso level. This paper examines the fall signatures observed by a Doppler radar at the two positions and evaluates their consistencies with respect to the fall directions and locations. The complementary characteristics of the fall signatures motivate the integration of the features from the ceiling mounted and torso level radars to improve the fall detection performance. Experimental results using the data collected of an elderly at a senior residence apartment for almost a year support our studies and the benefit of using both radars. The false alarm rate is reduced by a factor of 10 at 100% detection rate when compared to using a ceiling radar alone. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/AIS-170469 | JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS |
Keywords | Field | DocType |
Classifier,Doppler radar,fall detection,wavelet | Radar,Computer science,Simulation,Real-time computing | Journal |
Volume | Issue | ISSN |
10 | 1 | 1876-1364 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Yu Su | 1 | 33 | 3.23 |
| K.C. Ho | 2 | 1311 | 148.28 |
| Marilyn Rantz | 3 | 310 | 26.24 |
| Marjorie Skubic | 4 | 1045 | 105.36 |