Name
Title | ||
|---|---|---|
Deep learning-based water-intake estimation method using second half of swallowing sound. |
Abstract | ||
|---|---|---|
Super-aged societies are facing a staggering shortage of nurses and caregivers. Although water-intake is a necessity regarding healthcare management of elderly people, it is not currently automated. Thus, it is a burden on caregivers. We investigated how to estimate water intake by analyzing swallowing sounds. However, the estimation error for each subject was large because of the difficulty of discovering and extracting the common features correlated with appropriate water intake for subjects from complicated swallowing sounds. We thus propose a deep learning-based water-intake estimation method using the second half of a swallowing sound, which is correlated with water-intake. |
Year | Venue | Keywords |
|---|---|---|
2017 | IEEE Global Conference on Consumer Electronics | deep learning,water-intake estimation,swallowing sound,healthcare |
Field | DocType | ISSN |
Swallowing,Computer science,Feature extraction,Artificial intelligence,Deep learning,Physical medicine and rehabilitation,Health administration,Economic shortage | Conference | 2378-8143 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutaro Yamada | 1 | 0 | 0.34 |
| Masafumi Nishimura | 2 | 112 | 22.77 |
| Hiroshi Mineno | 3 | 130 | 34.93 |
| Takato Saito | 4 | 1 | 1.36 |
| Satoshi Kawasaki | 5 | 0 | 1.35 |
| Daizo Ikeda | 6 | 8 | 8.59 |
| Masaji Katagiri | 7 | 42 | 5.95 |