Kayne Duncanson
Higher Degree by Research Candidate
Adelaide Medical School
Faculty of Health and Medical Sciences
Our team is conducting multi-disciplinary research on the application of artificial intelligence to human gait analysis. Specifically, we are developing and evaluating machine learning models that identify people based on how they apply force through their feet while walking. Previously, as part of my Honours project, I contributed to a research project on the mechanical and energetic effects of altered arch stiffness during human locomotion. I am passionate about research areas at the intersection of Human and Computer Sciences, such as Artificial Intelligence, Biomechanics and Biometrics.
Our team is conducting multi-disciplinary research on the application of artificial intelligence to human gait analysis. Specifically, we are developing and evaluating machine learning models that identify people based on how they apply force through their feet while walking. Previously, as part of my Honours project, I contributed to a research project on the mechanical and energetic effects of altered arch stiffness during human locomotion. I am passionate about research areas at the intersection of Human and Computer Sciences, such as Artificial Intelligence, Biomechanics and Biometrics.
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Appointments
Date Position Institution name 2020 - 2023 PhD Candidate University of Adelaide -
Language Competencies
Language Competency English Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2014 - 2019 University of Queensland Australia Bachelor of Exercise and Nutrition Sciences (Honours) -
Certifications
Date Title Institution name Country 2020 Deep Learning Specialization deeplearning.ai - -
Research Interests
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Journals
Year Citation 2024 Duncanson, K. A., Horst, F., Abbasnejad, E., Hanly, G., Robertson, W. S. P., & Thewlis, D. (2024). Modelling individual variation in human walking gait across populations and walking conditions via gait recognition. Journal of the Royal Society Interface, 21(221), 20240565-1-20240565-15.
Europe PMC12023 Duncanson, K. A., Thwaites, S., Booth, D., Hanly, G., Robertson, W. S. P., Abbasnejad, E., & Thewlis, D. (2023). Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors, 23(7), 3392.
Scopus5 WoS1 Europe PMC32021 Duncanson, K., Thwaites, S., Booth, D., Abbasnejad, E., Robertson, W., & Thewlis, D. (2021). The Most Discriminant Components of Force Platform Data for Gait Based Person Re-identification.
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Datasets
Year Citation - Duncanson, K., Thwaites, S., Booth, D., Hanly, G., Robertson, W., Abbasnejad, E., & Thewlis, D. (n.d.). ForceID Dataset A.
DOI
Australian Government Research Training Program Stipend;
Defence Science and Technology Group;
National Health and Medical Research Council (ID: 1126229).
In 2019, I was a practical demonstrator for first and second year Biomechanics at the University of Queensland. I tutored 120+ students and assisted with examination marking. Prior to that, I tutored small groups at University of Queensland colleges and assisted over two-dozen independent students.
I love teaching because it allows me to meet new people and engage in mutual learning and inspiration through the sharing of knowledge and ideas (not just on content, but also on the philosophy and methodology of learning). Teaching is a great way to encourage new patterns of thought and behaviour that can redefine our lives.
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Memberships
Date Role Membership Country 2021 - ongoing Member International Society of Biomechanics Australia
Connect With Me
External Profiles
