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 = {Chen, S and Kang, L and Lu, Y and Wang, N and Lu, Y and Lo, B and Yang, G-Z},
doi = {10.1109/BSN.2019.8771090},
pages = {1--4},
publisher = {IEEE},
title = {Discriminative information added by wearable sensors for early screening - a case study on diabetic peripheral neuropathy},
url = {http://dx.doi.org/10.1109/BSN.2019.8771090},
year = {2019}

RIS format (EndNote, RefMan)

AB - Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly to the existing clinical standards, and boosted the C-index significantly from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.
AU - Chen,S
AU - Kang,L
AU - Lu,Y
AU - Wang,N
AU - Lu,Y
AU - Lo,B
AU - Yang,G-Z
DO - 10.1109/BSN.2019.8771090
EP - 4
PY - 2019///
SN - 2376-8886
SP - 1
TI - Discriminative information added by wearable sensors for early screening - a case study on diabetic peripheral neuropathy
UR - http://dx.doi.org/10.1109/BSN.2019.8771090
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492872400030&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/8771090
UR - http://hdl.handle.net/10044/1/75185
ER -