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Tremor Biomarkers

Contact: Dr Jane Alty

Applying Artificial Intelligence to identify new tremor biomarkers of dementia risk

There is an urgent need to develop non-invasive and inexpensive biomarkers of pre-clinical and early dementia that are accessible – including for those adults in rural and remote communities, and those with different languages and cultural backgrounds. We are developing a new online ‘computer tremor detection test’ to identify individuals with high risk of dementia for further evaluation with specialist blood biomarker tests, clinical assessment and targeted preventive interventions. Early identification of dementia risk would enable people to enter drug trials before cognition becomes impaired, and for targeted risk factor modification that could prevent 40% of dementia cases.

Late-onset tremor, or ‘Ageing-Related Tremor’ (ART) describes a specific pattern of hand tremor that looks similar to Essential Tremor (ET) but has a late-age onset and is associated with frailty and increased risk of dementia in the future. So far, spiral drawings and subjective clinical assessments by neurologists have been used to identify ART but these methods are limited by inter-rater variability and poor access to specialist clinicians worldwide. If we could develop a method to objectively detect and measure ART, this would have high potential to be a useful biomarker of dementia risk.


We will develop a computer test that objectively detects and measures ART by applying computer vision and other cutting edge artificial intelligence methods to online video-recordings of hand movements. Machine learning has previously been successfully applied to distinguish videos of Parkinson’s disease from ET and we will develop these computer methods to (i) distinguish ART from ET and from healthy ageing, (ii) delineate detailed characteristics of ART and (iii) determine how ART relates to dementia risk.

Research Team:


  • Dr Jane Alty (primary; Wicking Dementia Centre/School of Medicine, College of Health and Medicine)
  • A/Prof Quan Bai (Information and Communication Technology, College of Sciences and Engineering)
  • Dr Rebecca St George (Psychology, College of Health and Medicine)
  • Dr Son Tran (Information and Communication Technology, College of Sciences and Engineering)

PhD student:

  • Xinyi Wang (awarded a cross-college scholarship by College of Health and Medicine and College of Sciences and Engineering)