• CP2406: Programming III (Level 2; CNS)
  • CP3101: Professional Internship (Level 3; CNS & TSV)
  • CP3103: Independent Project (Level 3; CNS & TSV)
  • CP3405: Design Thinking and Project Management (Level 3; CNS & TSV)
  • CP3406: Mobile Computing (Level 3; CNS & TSV)
  • CP3408: Game Engine and Simulation (Level 3; TSV)
  • CP5307: Advanced Mobile Technology (Level 5; CNS & TSV)
  • 2017 - CSIRO STEM Professional
  • 2017 - Advance Queensland Community Digital Champion

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles
Conference Papers

ResearchOnline@JCU stores 37+ research outputs authored by Dr Jason Holdsworth from 2004 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Tropical Australian Academic Health Centre Limited - Research Seed Grants

Immersive virtual reality in a northern Queensland haemodialysis unit: A cross-over randomised controlled feasibility trial.

Indicative Funding
$18,000 over 2 years (administered by THHS)
This study will explore the feasibility and acceptability of an immersive VR experience for patients attending a north Queensland haemodialysis service and provide information to inform a multi-centre randomised controlled trial. Over the 4 week intervention period, participants will be offered a headset with vision of the local natural environment and with audio. Outcomes will be measured by participants: acceptability and usability of VR; attendance at scheduled dialysis sessions and adherence to lifestyle modifications; wellbeing, anxiety and depression; adverse events such as nausea. The feasibility and acceptability of the equipment from the clinicians? perspectives will also be explored.
Wendy Smyth, Cate Nagle, Joleen McArdle, John Body-Dempsey, Valli Manickam, Anne Swinbourne, Ickjai Lee and Jason Holdsworth (Townsville Hospital and Health Services, College of Healthcare Sciences and College of Science & Engineering)
Virtual reality; Haemodialysis; Distraction Therapy

Fisheries Research & Development Corporation - Annual Competitive Round

Application of a machine learning approach for effective stock management of abalone

Indicative Funding
$115,649 over 2 years
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R&D improvements. Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems, and render the product data to be accessible and applicable for farmers.
Jan Strugnell, Marcus Sheaves, Carlo Mattone, Ickjai Lee, Joanne Lee, Jason Holdsworth and Art (Hemmaphan) Suwanwiwat (College of Science & Engineering)
Abalone (Haliotidae); Machine Learning

World Wide Fund for Nature (US) - Contract Research

From Coastal Communities to Cloud Communities ? New Application and Artificial Intelligence to Monitor Fish Stocks Using Photos ? Application Development

Indicative Funding
$53,100 over 1 year
The Project aims at develop an artificial intelligence capable to autonomously identify fish species and number from images collected at fish markets in remote location, so that effective catch rate can be evaluated and management policies can be developed.
Marcus Sheaves, Carlo Mattone, Michael Bradley, Joanne Lee, Jason Holdsworth, Art (Hemmaphan) Suwanwiwat and Ickjai Lee (College of Science & Engineering)
Artificial Intelligence; Phone App; Caught Fish; Catch Rate

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

  • An Autonomous Tool to Produce Prototype Neural Networks for Deep Learning (PhD , Secondary Advisor)
  • Efficient Semantic Segmentation using Deep Learning. (PhD , Secondary Advisor)
  • Mobile Application for Fish Type Recognition (PhD , Secondary Advisor)
  • Development of high-level programming tools to improve the robustness of Internet of Things networks. (PhD , Secondary Advisor)
  • Enhancing adult learner engagement to online learning activities in higher education (PhD , Primary Advisor)
  • Exploring the Effects of Synchronous, Asynchronous and Blended Learning Approaches on Academic Learning Behaviours for Visual, Auditory and Kinaesthetic Learners (PhD , Primary Advisor)
  • Investigating the Pedagogical Efficacy of Current Tools used in the Teaching of Coding to different age groups (Masters , Secondary Advisor)
  • Designing physical interface for application of augmented reality in toddler toys (Masters , Secondary Advisor)

These are the most recent metadata records associated with this researcher. To see a detailed description of all dataset records, visit Research Data Australia.

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