Abstract
This paper describes user activity recognition for
lifestyle disease patients at home: ways to define data
mining system for sensing, logging, analyzing, mining,
measuring and recognizing user's daily activities.
Lifestyle disease patients spend most of the time at
home. There are lots of sensing data that can be based on
home devices with home networking (sensors, gadgets,
appliances, cameras, smart phones and some software
applications running on computers). Main problem is
interoperability, there is no standard framework for
logging, analyzing and utilizing the available data
sources. In this paper, we will introduce our layered
architecture to do data mining for user's activity
recognition. Understand user's life pattern can help
medical services to cure and prevent diseases from
developing.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Consumer Electronics, ICCE 2013 |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 276-277 |
| ISBN (Electronic) | 978-1-4673-1363-6, 978-1-4673-1362-9 |
| ISBN (Print) | 978-1-4673-1361-2 |
| DOIs | |
| Publication status | Published - 2013 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Consumer Electronics, ICCE 2013 - Las Vegas, United States Duration: 11 Jan 2013 → 14 Jan 2013 |
Conference
| Conference | IEEE International Conference on Consumer Electronics, ICCE 2013 |
|---|---|
| Abbreviated title | ICCE 2013 |
| Country/Territory | United States |
| City | Las Vegas |
| Period | 11/01/13 → 14/01/13 |