A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


Citation

BibTex format

@article{Zhang:2020:10.1109/JBHI.2019.2933046,
author = {Zhang, Y and Guo, Y and Yang, P and Chen, W and Lo, B},
doi = {10.1109/JBHI.2019.2933046},
journal = {IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS},
pages = {465--474},
title = {Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network},
url = {http://dx.doi.org/10.1109/JBHI.2019.2933046},
volume = {24},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AU - Zhang,Y
AU - Guo,Y
AU - Yang,P
AU - Chen,W
AU - Lo,B
DO - 10.1109/JBHI.2019.2933046
EP - 474
PY - 2020///
SN - 2168-2194
SP - 465
TI - Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
T2 - IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
UR - http://dx.doi.org/10.1109/JBHI.2019.2933046
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000516606600015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
VL - 24
ER -