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 = {Lo, B and Zhang, Y and Inan, OT and Ellul, J},
doi = {10.1109/jbhi.2019.2944778},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {2245--2246},
title = {Guest editorial: special issue on pervasive sensing and machine learning for mental health},
url = {http://dx.doi.org/10.1109/jbhi.2019.2944778},
volume = {23},
year = {2019}

RIS format (EndNote, RefMan)

AB - The seven papers included in this special section focus on machine learning applications for the mental health industry. Mental health is one of the major global health issues affecting substantially more people than other noncommunicable diseases. Much research has been focused on developing novel technologies for tackling this global health challenge, including the development of advanced analytical techniques based on extensive datasets and multimodal acquisition for early detection and treatment of mental illnesses. The papers in this issue are dedicated to cover the related topics on technological advancements for mental health care and diagnosis with a focus on pervasive sensing and machine learning.
AU - Lo,B
AU - Zhang,Y
AU - Inan,OT
AU - Ellul,J
DO - 10.1109/jbhi.2019.2944778
EP - 2246
PY - 2019///
SN - 2168-2194
SP - 2245
TI - Guest editorial: special issue on pervasive sensing and machine learning for mental health
T2 - IEEE Journal of Biomedical and Health Informatics
UR - http://dx.doi.org/10.1109/jbhi.2019.2944778
UR - https://ieeexplore.ieee.org/document/8894188
UR - http://hdl.handle.net/10044/1/75219
VL - 23
ER -