Sensing

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.

2 column colour block - Research areas

Body Sensor Networks

Body Sensor Networks

The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wireless wearable devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices.
Surgical Implants

Surgical Implants

One of the major focuses of the Hamlyn Centre is the development of miniaturised sensors embodied either as smart surgical appliances or implants to enhance the safety of surgical procedures, enhance patient care, and also the patient's quality of life.

2 column colour block - Research areas 2

ESPRIT

IoT and Smart Environments

Our main research in IoT and smart environments is focussed on the challenges of ambient sensing through the introduction of novel sensing platforms, self-configuration approaches, self-adaptive techniques, and seamless integration with wearable sensors.
Smart Environments

ESPRIT

The vision of ESPRIT is to position UK at the forefront of pervasive sensing in elite sports and promote its wider application in public life-long health, wellbeing and healthcare. Our work addresses some of the key challenges related to miniaturised wearable sensors for sports.

Citation

BibTex format

@inproceedings{Han:2021,
author = {Han, J and Gu, X and Lo, B},
publisher = {IEEE},
title = {Semi-supervised contrastive learning for generalizable motor imagery eeg classification},
url = {http://hdl.handle.net/10044/1/90332},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Electroencephalography (EEG) is one of the most widely used brain-activity recording methods in non-invasive brain-machine interfaces (BCIs). However, EEG data is highly nonlinear, and its datasets often suffer from issues such as data heterogeneity, label uncertainty and data/label scarcity. To address these, we propose a domain independent, end-to-end semi-supervised learning framework with contrastive learning and adversarial training strategies. Our method was evaluated in experiments with different amounts of labels and an ablation study in a motor imagery EEG dataset. The experiments demonstrate that the proposed framework with two different backbone deep neural networks show improved performance over their supervised counterparts under the same condition.
AU  - Han,J
AU  - Gu,X
AU  - Lo,B
PB  - IEEE
PY  - 2021///
TI  - Semi-supervised contrastive learning for generalizable motor imagery eeg classification
UR  - http://hdl.handle.net/10044/1/90332
ER  -
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