Name
Abstract | ||
|---|---|---|
This research aims at building a fall detection system for the elderly at home. In addition, this system reduces uncomfortable feeling by not attaching sensors to the human bodies. In the previous research, RFID tags with sensing capability were already used to detect behaviors. However, the previous system detects only behaviors such as standing up and sitting down while using a wheelchair. The purpose of the proposing system is to detect falls while walking. For that purpose, passive RFID tags are employed to the room shoes in order to monitor the motions of the elderly and simultaneously detect his/her accidents. This paper presents the system for data collection and feature extraction, using machine learning techniques, and experimental results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/GCCE.2018.8574720 | 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) |
Keywords | Field | DocType |
fall detection,RFID,machine learning | Wheelchair,Data collection,Computer vision,Computer science,Feature extraction,Artificial intelligence,Sitting | Conference |
ISSN | ISBN | Citations |
2378-8143 | 978-1-5386-6310-3 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koichi Toda | 1 | 0 | 0.34 |
| Norihiko Shinomiya | 2 | 50 | 19.15 |