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

@article{Thompson:2018:10.1364/OE.26.014186,
author = {Thompson, AJ and Power, M and Yang, G-Z},
doi = {10.1364/OE.26.014186},
journal = {Optics Express},
pages = {14186--14200},
title = {A micro-scale fiber-optic force sensor fabricated using direct laser writing and calibrated using machine learning},
url = {http://dx.doi.org/10.1364/OE.26.014186},
volume = {26},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Fiber-optic sensors have numerous existing and emerging applications spanning areas from industrial process monitoring to medical diagnosis. Two of the most common fiber sensors are based on the fabrication of Bragg gratings or Fabry-Perot etalons. While these techniques offer a large array of sensing targets, their utility can be limited by the difficulties involved in fabricating forward viewing probes (Bragg gratings) and in obtaining sufficient signal-to-noise ratios (Fabry-Perot systems). In this article we present a microscale fiber-optic force sensor produced using direct laser writing (DLW). The fabrication entails a single-step process that can be undertaken in a reliable and repeatable manner using a commercial DLW system. The sensor is made of a series of thin plates (i.e. Fabry-Perot etalons), which are supported by springs that compress under an applied force. At the proximal end of the fiber, the interferometric changes that are induced as the sensor is compressed are read out using reflectance spectroscopy, and the resulting spectral changes are calibrated with respect to applied force. This calibration is performed using either singular value decomposition (SVD) followed by linear regression or artificial neural networks. We describe the design and optimization of this device, with a particular focus on the data analysis required for calibration. Finally, we demonstrate proof-of-concept force sensing over the range 0-50 μN, with a measurement error of approximately 1.5 μN.
AU  - Thompson,AJ
AU  - Power,M
AU  - Yang,G-Z
DO  - 10.1364/OE.26.014186
EP  - 14200
PY  - 2018///
SN  - 1094-4087
SP  - 14186
TI  - A micro-scale fiber-optic force sensor fabricated using direct laser writing and calibrated using machine learning
T2  - Optics Express
UR  - http://dx.doi.org/10.1364/OE.26.014186
UR  - http://hdl.handle.net/10044/1/59158
VL  - 26
ER  -
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