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.

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

@article{Lo:2020:10.1109/tii.2019.2942831,
author = {Lo, FP-W and Sun, Y and Qiu, J and Lo, BPL},
doi = {10.1109/tii.2019.2942831},
journal = {IEEE Transactions on Industrial Informatics},
pages = {577--586},
title = {Point2Volume: A vision-based dietary assessment approach using view synthesis},
url = {http://dx.doi.org/10.1109/tii.2019.2942831},
volume = {16},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.
AU  - Lo,FP-W
AU  - Sun,Y
AU  - Qiu,J
AU  - Lo,BPL
DO  - 10.1109/tii.2019.2942831
EP  - 586
PY  - 2020///
SN  - 1551-3203
SP  - 577
TI  - Point2Volume: A vision-based dietary assessment approach using view synthesis
T2  - IEEE Transactions on Industrial Informatics
UR  - http://dx.doi.org/10.1109/tii.2019.2942831
UR  - https://ieeexplore.ieee.org/document/8853329
UR  - http://hdl.handle.net/10044/1/76639
VL  - 16
ER  -
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