Back to Search
Start Over
System Design for Big Data Application in Emotion-Aware Healthcare
- Source :
- IEEE Access, Vol 4, Pp 6901-6909 (2016)
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- As the living standards improve and the health consciousness enhances, the healthcare industry has become a hot spot in nowadays society and some health monitoring systems emerge one after another in recent years. However, the mostly existing systems only focus on the logic reasoning but ignore the factor of the user's emotion, which is regarded as an important factor to impact human health. In this paper, we design a system for big data application in emotion-aware healthcare (BDAEH), which pays attention to both the logic reasoning and the emotion computing. Meanwhile, the SDN the and 5G technologies are adopted in the BDAHE system to improve the resource utilization and the overall network performance of the system. The BDAEH system includes the following functions: healthcare data collection, healthcare data transmission, healthcare data storage, healthcare data analysis, and human-machine interaction. The healthcare data are generated by wearable devices or sensing-less sensors, and these healthcare data are regarded as the foundation to expand a series of data processing. The healthcare data transmission is performed through leveraging the SDN and the 5G technologies. In the data center, the related technologies based on cloud computing are utilized to store and analyze healthcare data, which obtains both the emotion and the health state of the users, and the relation between the emotion and the illness. Finally, the BDAEH system returns the analysis result to the users or the doctors for further treatment schemes or rehabilitation advice. The presented system is expected to validly improve the healthcare services by considering the emotion factor.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.18c980928d974d2e8e2f944e82527b55
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2016.2616643