WBAN Path Loss Based Approach For Human Activity Recognition With Machine Learning Techniques
- Resource Type
- Conference
- Authors
- Negra, Rim; Jemili, Imen; Zemmari, Akka; Mosbah, Mohamed; Belghith, Abdelfettah
- Source
- 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) Wireless Communications & Mobile Computing Conference (IWCMC), 2018 14th International. :470-475 Jun, 2018
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Activity recognition
Wireless communication
Body area networks
Medical services
Machine learning
Support vector machines
Forestry
WBAN
Path Loss
Machine Learning
activity recognition
- Language
- ISSN
- 2376-6506
Wireless Body Area Networks are nowadays attracting both academic and industrial worlds. Combining collected data related to patient context with original health measurement can enhance the general health state monitoring and help to better understand the patient disease evolution. Daily activity is one of the important features that may influence the patient health state. Thus, recognizing the user activity can be a useful way for improving quality of health services. Relying on supervised learning, we study the feasibility of extracting and classifying the human activities from channel gain measures, which is an important feature that characterizes the WBAN channel links.