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{Lo:2019:10.1109/BSN.2019.8771089,
author = {Lo, FP-W and Sun, Y and Qiu, J and Lo, B},
doi = {10.1109/BSN.2019.8771089},
publisher = {IEEE},
title = {A novel vision-based approach for dietary assessment using deep learning view synthesis},
url = {http://dx.doi.org/10.1109/BSN.2019.8771089},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Dietary assessment system has proven as an effective tool to evaluate the eating behavior of patients suffering from diabetes and obesity. To assess the dietary intake, the traditional method is to carry out a 24-hour dietary recall (24HR), a structured interview aimed at capturing information on food items and portion size consumed by participants. However, unconscious biases are developed easily due to individual's subjective perception in this self-reporting technique which may lead to inaccuracy. Thus, this paper proposed a novel vision-based approach for estimating the volume of food items based on deep learning view synthesis and depth sensing techniques. In this paper, a point completion network is applied to perform 3D reconstruction of food items using a single depth image captured from any convenient viewing angle. Compared to previous approaches, the proposed method has addressed several key challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity. Experiments have been carried out to examine this approach and showed the feasibility of the algorithm in accurate estimation of food volume.
AU  - Lo,FP-W
AU  - Sun,Y
AU  - Qiu,J
AU  - Lo,B
DO  - 10.1109/BSN.2019.8771089
PB  - IEEE
PY  - 2019///
SN  - 2376-8886
TI  - A novel vision-based approach for dietary assessment using deep learning view synthesis
UR  - http://dx.doi.org/10.1109/BSN.2019.8771089
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492872400029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8771089
UR  - http://hdl.handle.net/10044/1/75193
ER  -
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