Welcome to IOT Health

The IoT-Health research program aims to capitalise on advancements in technology along with sophisticated information and data processing to better understand disease progression in chronic health conditions. This research program couples IoT processes with Machine Learning (Deep Learning Neural Networks), to gather passive data about human health and then use this information to develop predictive algorithms for chronic diseases, such as mental illness and cancer.

Team

Meet our team who has been our strenght.

Lead of the Program

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DR RAJIB RANA
[PhD UNSW, BSC KU with Gold Medals]
SENIOR RESEARCH FELLOW - Senior Lecturer, Computing

Academic Members

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DR RAVINESH DEO (SENIOR LECTURER)

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JEFFREY SOAR (PROFESSOR)

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JI ZHNAG (SENIOR LECTURER)

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DR OMAR ALI (LECTURER)

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Dr MICHAEL LANE (SENIOR LECTURER)

Students

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SHEZAN

Phd Candidate

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SIDDIQUE LATIF

PhD Candidate

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MONA NOUROOZIFAR

PhD Candidate

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KARTHIKEYAN

Software Engineer

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AHSAN MEHEDI

Masters Leading to PhD candidate

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THEJAN RAJAPAKSHE

PhD Candidate

Collaborators

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PROFESSOR GERALD HUMPHRIS

Chair in Health Psychology University of St Andrews

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DR NIC LANE

Senior Lecturer at University College London (UCL), and leader of DeepX (an embedded focused deep learning unit) at Nokia Bell Labs.

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PROFESSOR ANDREW WHITEHOUSE

Director, Autism Research, Telethon Kids Institute, Autism, CRC

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PROFESSOR MICHAEL BREAKSPEAR

Head, Systems Neuroscience Group, QIMR

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DR RAJA JURDAK

Senior Principal Research Scientist, Group Leader, Distributed Sensing Systems Group, CSIRO

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PROFESSOR ROLAND GOECKE

Leader, Vision Sensing Group, University of Canberra

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PROFESSOR XUE LI

Big Data Expert, UQ

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A/PROFESSOR JULIEN EPPS

Associate Professor, Digital Signal Processing, UNSW, Sydney

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PROFESSOR NEIL BERGMANN

Chair in Embedded Systems, UQ

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A/PROFESSOR JOHN REILLY

Medical Director, Townsville Hospital Mental Health Service Group, Queensland Health

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PROFESSOR JEFFREY KAYE

Director, Layton Aging and Alzheimer's Disease Center, Director, Oregon Center for Aging and Technology (ORCATECH), Oregon Health and Science University, USA

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DR PETER JACOBS

Director, Jacobs Lab, Oregon Health and Science University, USA

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A/PROFESSOR NIRUPAMA BULUSU

Associate Professor, Computer Science @ Portland State University, USA.

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A/PROFESSOR JUNAID QADIR

Associate Professor, Information Technology University, Punjab, Lahore

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PROFESSOR MARGOT BRERETON (QUT)

Professor - ‎Computer Human Interaction

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DAVID KAVANAGH

PROFESSOR, QUT

Projects

Mood Inference

This project aims to infer mood from "phone conversation" without any human intervention. Deep Learning Neural Networks would be the core of this development, with a major focus on "privacy" and "robust" computing.

This project has great commercial value and is central to many projects being developed in this lab. A completely novel method is being developed, which will be first of its kind

Distress Detection in Cancer Patients

The project aims to develop an automated system to detect “distress” in cancer patients enabling early referral to interventions, to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment, which would significantly improve quality of life for individuals and reduce the costs on the health care system.

Ear-Phone

In this project, we proposed a noise mapping system using the crowdsensed noise samples. The key challenge that we addressed is the noise map reconstruction from incomplete data caused by uncoordinated movement of the participants in space and time. In 2006, the theory of Compressive Sensing (CS) also started to emerge. The CS theory showed that if a signal or a data vector is sparse in some representation domain (e.g., DCT, Fourier etc.), the signal or vector can be recovered from a small number of measurements (projections) of that vector. This was a just-in-time intervention for the problem considered. However, there was a major hiccup that in the CS theory it was required to take the measurements across the whole signal or vector. In our scenario, we could never collect all the samples. We proposed a new compressive sensing framework where this requirement was not necessary and showed that using some additional measurements (compared to the number of measurements required by the original theory of compressive sensing) it is possible to recover a noise map with reasonable accuracy.

Noise sampling without contextual information (such as the phone in the pocket, phone in bag etc.) does not offer great value as the contextual information helps to isolate the noisy measurements caused by the attenuation of sound in some sensing contexts, such bags. As an extension to the noise mapping work, we developed a context discovery approach, which using a multi-modal sensing approach offered an automatic discovery of the sensing contexts so that the sensing decisions can be dynamically adapted to avoid sound attenuation. We achieved 85% accuracy in classifying various sensing contexts.

Publications

2019

2018

2017

2016

2015

  • Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems
  • R Rana, B Kusy, J Wall, W Hu

    Energy 93, 245-255

  • Ear-Phone: A context-aware noise mapping using smart phones
  • R Rana, CT Chou, N Bulusu, S Kanhere, W Hu

    Pervasive and Mobile Computing 17, 1-22

  • Sparse Bayesian Learning for EEG Source Localization
  • S Saha, F de Hoog, YI Nesterets, R Rana, M Tahtali, TE Gureyev

    arXiv preprint arXiv:1501.04621

  • Optimal sampling strategy enabling energy-neutral operations at rechargeable wireless sensor networks
  • R Rana, W Hu, CT Chou

    IEEE Sensors Journal 15 (1), 201-208

  • Guiding Ebola patients to suitable health facilities: an SMS-based approach
  • MA Trad, R Jurdak, R Rana

    F1000Research 4

  • Simpletrack: Adaptive trajectory compression with deterministic projection matrix for mobile sensor networks
  • R Rana, M Yang, T Wark, CT Chou, W Hu

    IEEE Sensors Journal 15 (1), 365-373

2014

  • Experiences with Sensors for Energy Efficiency in Commercial Buildings
  • B Kusy, R Rana, P Valencia, R Jurdak, J Wall

    Real-World Wireless Sensor Networks, 231-243

2013

  • Real-time classification via sparse representation in acoustic sensor networks
  • B Wei, M Yang, Y Shen, R Rana, CT Chou, W Hu

    Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 21

  • Feasibility analysis of using humidex as an indoor thermal comfort predictor
  • R Rana, B Kusy, R Jurdak, J Wall, W Hu

    Energy and Buildings 64, 17-25

  • Determination of activities of daily living of independent living older people using environmentally placed sensors
  • Q Zhang, M Karunanithi, R Rana, J Liu

    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual …

  • Nonuniform compressive sensing for heterogeneous wireless sensor networks
  • Y Shen, W Hu, R Rana, CT Chou

    IEEE Sensors journal 13 (6), 2120-2128

2012

  • Distributed sparse approximation for frog sound classification
  • B Wei, M Yang, RK Rana, CT Chou, W Hu

    Proceedings of the 11th international conference on Information Processing …

2011

  • Towards plug-and-play functionality in low-cost sensor network
  • R Rana, N Bergmann, J Trevathan

    Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP …

  • Compressive sensing for gait recognition
  • S Sivapalan, RK Rana, D Chen, S Sridharan, S Denmon, C Fookes

    Digital Image Computing Techniques and Applications (DICTA), 2011 …

  • Non-uniform compressive sensing in wireless sensor networks: Feasibility and application
  • Y Shen, W Hu, R Rana, CT Chou

    Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP …

  • Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes
  • J Xu, S Denman, S Sridharan, C Fookes, R Rana

    Proceedings of the 2011 joint ACM workshop on Modeling and representing …

  • Sparse temporal representations for facial expression recognition
  • SW Chew, R Rana, P Lucey, S Lucey, S Sridharan

    Pacific-Rim Symposium on Image and Video Technology, 311-322

  • Sparse Approximation Methods For Wireless Sensor Networks
  • R Rana

    LAP Lambert Academic Publishing

  • Addressing three wireless sensor network challenges using sparse approximation methods
  • RK RANA

    University of New South Wales, Faculty of Engineering, Computer Science …

  • An adaptive algorithm for compressive approximation of trajectory (aacat) for delay tolerant networks
  • R Rana, W Hu, T Wark, CT Chou

    European Conference on Wireless Sensor Networks, 33-48

2010

  • Ear-phone: an end-to-end participatory urban noise mapping system
  • RK Rana, CT Chou, SS Kanhere, N Bulusu, W Hu

    Proceedings of the 9th ACM/IEEE International Conference on Information …

  • Energy-aware sparse approximation technique (east) for rechargeable wireless sensor networks
  • R Rana, W Hu, CT Chou

    European Conference on Wireless Sensor Networks, 306-321

2009

  • Ear-Phone assessment of noise pollution with mobile phones
  • RK Rana, CT Chou, S Kanhere, N Bulusu, W Hu

    Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems …

  • Energy efficient information collection in wireless sensor networks using adaptive compressive sensing
  • CT Chou, R Rana, W Hu

    Local Computer Networks, 2009. LCN 2009. IEEE 34th Conference on, 443-450

2007

  • Reconstruction of temporal-spatial profile from participatory sensing data using Compressive Sensing
  • RK Rana, CT Chou, S Kanhere

Others

  • Mapping System
  • RK Rana, CT Chou, SS Kanhere, N Bulusu, W Hu

  • Opportunistic and Context-aware Affect Sensing on Smartphones: The Concept, Challenges and Opportunities1
  • R Rana, M Hume, J Reilly, R Jurdak, J Soar

  • Context-aware Mood Mining
  • R Rana, R Jurdak, X Li, J Soar, R Goecke, J Epps, M Breakspear

  • A Novel Mood Mining Approach using Deep Learning and Sparse Random Classifier
  • R Rana

News

  • Rajib Rana has been chosen by Australian Institute of Policy and Science as one of Queensland's most outstanding scientists for achievements in the area of scientific research and communication.
  • Rajib Rana received Prestigious Advance Queensland Fellowship to develop a relapse prediction from Mood
  • The IoT-Health program receives USQ strategic funds to purchase a High-Performance Computer.
  • The IoT-Health program starts a new collaboration with Hospitals in Metro North Mental Health includes Royal Brisbane Women’s hospital and Prince Charles Hospital.
  • Siddique Latif joins IoT-Health program
  • Ahsan Rossy joins IoT-Health program
  • Thejan Rajapakshe joins IoT-Health program
  • Shezan Haque joins IoT-Health program

Funding

Our Alumni

Students who have worked with us on different projects

Bruno Kosawa

Android Developer, OLX Brazil

Davis Une Miyashiro

Android Developer, Qudini Ltd, London, UK

Karthikeyan Doraisamy

Systems Engineer, Akamai, India

Sajib Saha (PhD, UNSW)

Research Scientist CSIRO

IOT HEALTH

The IoT-Health research program aims to capitalise on advancements in technology along with sophisticated information and data processing to better understand disease progression in chronic health conditions.

Office: B341
Institute of Resilient Regions
University of Southern Queensland
Cnr Education City Drive
Springfield, QLD 4300

rajib.rana@usq.edu.au

+61 7 3470 4234