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 = {Andreu-Perez, J and Leff, DR and Shetty, K and Darzi, A and Yang, GZ},
doi = {10.1089/brain.2015.0350},
journal = {Brain Connectivity},
pages = {375--388},
title = {Disparity in frontal lobe connectivity on a complex bimanual motor task aids in classification of operator skill level.},
url = {http://dx.doi.org/10.1089/brain.2015.0350},
volume = {6},
year = {2016}

RIS format (EndNote, RefMan)

AB - Objective metrics of technical performance (e.g., dexterity, time, and path length) are insufficient to fully characterize operator skill level, which may be encoded deep within neural function. Unlike reports that capture plasticity across days or weeks, this articles studies long-term plasticity in functional connectivity that occurs over years of professional task practice. Optical neuroimaging data are acquired from professional surgeons of varying experience on a complex bimanual coordination task with the aim of investigating learning-related disparity in frontal lobe functional connectivity that arises as a consequence of motor skill level. The results suggest that prefrontal and premotor seed connectivity is more critical during naïve versus expert performance. Given learning-related differences in connectivity, a least-squares support vector machine with a radial basis function kernel is employed to evaluate skill level using connectivity data. The results demonstrate discrimination of operator skill level with accuracy ≥0.82 and Multiclass Matthew's Correlation Coefficient ≥0.70. Furthermore, these indices are improved when local (i.e., within-region) rather than inter-regional (i.e., between-region) frontal connectivity is considered (p = 0.002). The results suggest that it is possible to classify operator skill level with good accuracy from functional connectivity data, upon which objective assessment and neurofeedback may be used to improve operator performance during technical skill training.
AU - Andreu-Perez,J
AU - Leff,DR
AU - Shetty,K
AU - Darzi,A
AU - Yang,GZ
DO - 10.1089/brain.2015.0350
EP - 388
PY - 2016///
SN - 2158-0022
SP - 375
TI - Disparity in frontal lobe connectivity on a complex bimanual motor task aids in classification of operator skill level.
T2 - Brain Connectivity
UR - http://dx.doi.org/10.1089/brain.2015.0350
UR - http://hdl.handle.net/10044/1/31512
VL - 6
ER -