Name
Abstract | ||
|---|---|---|
Falling down is a great threat to the health of the elderly. Existing approaches for fall detection, such as threshold methods and offline classification methods, have been shown to be useful for providing emergency medical care for the elderly. However, those offline models lack generalization and adaptability for all the users in everyday life, which severely restricts their applications in real life situations. In this paper, we propose a cloud computing based fall detection framework which can update the model online with two-stage incremental update step. By applying cloud computing, the framework can take advantage of wearable sensors for more accurate and efficient fall detection. The proposed framework is comprised of a two-stage incremental update, which consists of local and cloud components. The local component updates the detection model with feedback from users, which can make the model more personalized for users in a timely manner. In the cloud component, the model can achieve self-improvement based on the data of daily livings collected from other users. Our simulation experiments show that our framework can achieve higher precision and recall with the incremental update based on data from all users. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SmartWorld.2018.00093 | 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) |
Keywords | Field | DocType |
Fall Detection,Wearable Device,Activity Recognition,Pervasive Computing | Adaptability,Everyday life,Activity recognition,Wearable computer,Computer science,Precision and recall,Real-time computing,Ubiquitous computing,Cloud computing,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-9381-0 | 0 | 0.34 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfei Shen | 1 | 4 | 4.21 |
| Yiqiang Chen | 2 | 1446 | 109.32 |
| Zhiqi Shen | 3 | 1148 | 82.57 |
| Si-Yuan Liu | 4 | 31 | 8.55 |