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 = {Ravi, D and Wong, C and Lo, B and Yang, G},
doi = {10.1109/JBHI.2016.2633287},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {56--64},
title = {A deep learning approach to on-node sensor data analytics for mobile or wearable devices},
url = {http://dx.doi.org/10.1109/JBHI.2016.2633287},
volume = {21},
year = {2016}

RIS format (EndNote, RefMan)

AB - The increasing popularity of wearable devices inrecent years means that a diverse range of physiological and functionaldata can now be captured continuously for applicationsin sports, wellbeing, and healthcare. This wealth of informationrequires efficient methods of classification and analysis wheredeep learning is a promising technique for large-scale data analytics.Whilst deep learning has been successful in implementationsthat utilize high performance computing platforms, its use onlow-power wearable devices is limited by resource constraints.In this paper, we propose a deep learning methodology, whichcombines features learnt from inertial sensor data together withcomplementary information from a set of shallow features toenable accurate and real-time activity classification. The design ofthis combined method aims to overcome some of the limitationspresent in a typical deep learning framework where on-nodecomputation is required. To optimize the proposed method forreal-time on-node computation, spectral domain pre-processingis used before the data is passed onto the deep learning framework.The classification accuracy of our proposed deep learningapproach is evaluated against state-of-the-art methods using bothlaboratory and real world activity datasets. Our results show thevalidity of the approach on different human activity datasets,outperforming other methods, including the two methods usedwithin our combined pipeline. We also demonstrate that thecomputation times for the proposed method are consistent withthe constraints of real-time on-node processing on smartphonesand a wearable sensor platform.
AU - Ravi,D
AU - Wong,C
AU - Lo,B
AU - Yang,G
DO - 10.1109/JBHI.2016.2633287
EP - 64
PY - 2016///
SN - 2168-2208
SP - 56
TI - A deep learning approach to on-node sensor data analytics for mobile or wearable devices
T2 - IEEE Journal of Biomedical and Health Informatics
UR - http://dx.doi.org/10.1109/JBHI.2016.2633287
UR - http://hdl.handle.net/10044/1/42700
VL - 21
ER -