Title :
Source recovery for body sensor network
Author :
Lo, Benny ; Deligianni, Fani ; Yang, Guang-Zhong
Author_Institution :
R. Soc.-Wolfson MIC Lab., Imperial Coll., London
Abstract :
To accurately capture clinically relevant episodes with body sensor networks (BSNs), multi-sensor fusion is essential for extracting intrinsic physiological and contextual information. Due to the heterogeneous nature of the sensors compounded by the mixture of signals across different sensor channels, this process can be practically difficult. The purpose of this paper is to describe the use of source separation for BSN based on independent component analysis (ICA). We demonstrate how this can be used in practical BSN experiments when the number of sensing channels is limited
Keywords :
blind source separation; independent component analysis; medical signal processing; sensor fusion; wireless sensor networks; blind source separation; body sensor network; contextual information; independent component analysis; intrinsic physiological information; multi-sensory data fusion; sensor channels; source recovery; Biomedical monitoring; Blind source separation; Body sensor networks; Data mining; Electrocardiography; Independent component analysis; Microwave integrated circuits; Sensor phenomena and characterization; Sensor systems; Source separation; Multi-sensory data fusion; blind source separation; source recovery;
Conference_Titel :
Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7695-2547-4
DOI :
10.1109/BSN.2006.51
