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.


BibTex format

author = {Ravi, D and Wong, C and Deligianni, F and Berthelot, M and Andreu-Perez, J and Lo, B and Yang, G},
doi = {10.1109/JBHI.2016.2636665},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {4--21},
title = {Deep learning for health informatics},
url = {http://dx.doi.org/10.1109/JBHI.2016.2636665},
volume = {21},
year = {2017}

RIS format (EndNote, RefMan)

AB - With a massive influx of multimodality data, the roleof data analytics in health informatics has grown rapidly in thelast decade. This has also prompted increasing interests in thegeneration of analytical, data driven models based on machinelearning in health informatics. Deep learning, a technique withits foundation in artificial neural networks, is emerging in recentyears as a powerful tool for machine learning, promising toreshape the future of artificial intelligence. Rapid improvementsin computational power, fast data storage and parallelization havealso contributed to the rapid uptake of the technology in additionto its predictive power and ability to generate automaticallyoptimized high-level features and semantic interpretation fromthe input data. This article presents a comprehensive up-to-datereview of research employing deep learning in health informatics,providing a critical analysis of the relative merit and potentialpitfalls of the technique as well as its future outlook. The papermainly focuses on key applications of deep learning in the fields oftranslational bioinformatics, medical imaging, pervasive sensing,medical informatics and public health.
AU - Ravi,D
AU - Wong,C
AU - Deligianni,F
AU - Berthelot,M
AU - Andreu-Perez,J
AU - Lo,B
AU - Yang,G
DO - 10.1109/JBHI.2016.2636665
EP - 21
PY - 2017///
SN - 2168-2208
SP - 4
TI - Deep learning for health informatics
T2 - IEEE Journal of Biomedical and Health Informatics
UR - http://dx.doi.org/10.1109/JBHI.2016.2636665
UR - http://hdl.handle.net/10044/1/42964
VL - 21
ER -