The IoT Health Program

[PC: Alberto Frigo - month of activities where each line is a day]
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Current Projects - Attention Prospective PhD students. 

Project 1: Mood Inference Engine.

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. 
Collaborators: Dr Nic Lane (Bell Labs), Dr Julien Epps (UNSW), Professor Margot Brereton (QUT), Dr Raja Jurdak (CSIRO), Professor Roland Goecke (UC), Professor Michael Breakspear (QIMR)
Industry Partners: Queensland Health, Cogninet, Nexus e-care.
PhD Student sought: Yes.

Project 2: Relapse Prediction in Mental Illness.

This project aims to develop a relapse prediction system on a smartphone for mental health patients. It will "passively" infer information about four domains (1) mood, (2) sleep, (3) physical activity and (4) social connectedness and will profile a person based on these. A suit of algorithms will be developed which will continuously run in the background and detect any "persistent change" in those domains and will attempt to predict a relapse based on those changes. 
Collaborators: Dr Nic Lane (Bell Labs), Dr Julien Epps (UNSW), Professor Margot Brereton (QUT), Dr Raja Jurdak (CSIRO), Professor Roland Goecke (UC), Professor Michael Breakspear (QIMR)
Industry Partners: Queensland Health, Nexus e-care.
PhD Student sought: Yes.

Project 3: Doctor's Resilience.

This project is led by Dr Michael Ireland at the Innovative Mental Health Service group at USQ. Doctors especially emergency doctors have long hours of stressful job, which often causes burnout leading to an error in treatment, which can cost even lives. This project aims at predict burnouts so that interventions can be offered to doctors to prevent the burnout to occur. The technical side of this project is of interest to me - i.e., burnout prediction. Various physiological sensor data from wearable sensors will be used to model the behavior using Deep Learning models and any persistent deviation will be identified and mapped to burnout for developing a prediction model. 
This project has serious merit as this will not be limited to doctors but will span to other emergency professionals - firefighters, police etc. In particular, in Australia even in general employment sectors, there is a push for maintaining employee health and wellbeing. So this technology will be widespread in near future.
PhD Student sought: Yes.

Project 4: Adherence prediction for chronic disease patients.

This project is led by Dr Sonja March, the director of the Innovative Mental Health Service Program at USQ. Chronic disease patients often fail to adhere to medication, which causes further complications and increased disease burdens. This project aims to predict the non-adherence behavior so that interventions can be brought into place early enough. My focus is on using wearable sensor data both physiological and behavioral and using advanced deep learning algorithms to predict the non-adherence behavior. 
PhD Studnet sought: Yes.

Project 5: Mood incorporated recommendation systems.

Recommendation Systems are quite popular nowadays. Highly successful companies like Netflix earn their 75% of revenue from recommendations. A mood-aware recommendations can be more appropriate and could achieve higher accuracy. This project aims to develop a mood-aware recommendation system and validate its impact on a recommendation.  
PhD Student sought: Yes.

Project 6: Marker bias from mood inference.

Recent studies validate that Mood has a profound impact on marking. Poor marking can cause dropouts at the college and universities and is a threat to the Australian economy. The project aims to quantify the impact of mood on marking, which will eventually contribute towards developing a marking bias cancellation system. 
PhD Student sought: Yes.
People

Dr Rajib Rana is a Vice Chancellor's Research Fellow 
and the lead of the IoT-Health lab.

Academic Members

Dr Ravinesh Deo



Professor Jeffrey Soar



Associate Professor Ji Zhnag


Omar Ali


Students

Arlen Rowe


         PhD Candidate

Ghazal Barghadi


         PhD Candidate

Mona Nouroozifar

           
          PhD Candidate




Siddique Latif

         
           Masters Student

Raja Azmat Abbas

    
       Masters Student

Collaborators

Dr Nic Lane

 

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


Professor Andrew Whitehouse






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







Professor Michael Breakspear





Head, Systems Neuroscience Group, QIMR 






Dr Raja Jurdak





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

Professor Roland Goecke





Leader, Vision Sensing Group, University of Canberra

Professor Xue Li





Big Data Expert, UQ

A/Professor Julien Epps




Associate Professor, 
Digital Signal Processing, UNSW, Sydney

Professor Neil Bergmann





Chair in Embedded Systems, UQ

A/Professor John Reilly




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

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

Dr Peter Jacobs





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

A/Professor Nirupama Bulusu





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

A/Professor Junaid Qadir




Associate Professor, 
Information Technology University, Punjab, Lahore