Technological Evolution in the Instrumentation of Ataxia Severity Measurement

Thang Ngo, Pubudu N. Pathirana, Malcolm K. Horne, Louise A. Corben, Ian H. Harding, David J. Szmulewicz

Research output: Contribution to journalArticleResearchpeer-review


Cerebellar ataxia is the poorly coordinated movement that results from injury or disease affecting the cerebellum. The diagnosis and assessment of ataxia are significantly challenging due to dependency on clinicians’ experience and the attendant subjectivity of such a process. In recent years, neuroimaging and sensor-based approaches, supported by effective machine learning techniques have made advances in the pursuit of addressing these clinical challenges. In this work, we present an outline of approaches to applying machine learning to this clinical challenge. We first provide a fundamental clinical overview with practical problems and then from a machine learning perspective, outline possible approaches with which to address these clinical challenges. Also discussed are the limitations in existing methods, the provision of cross disciplinary approaches and the current state-of-the-art as a potential basis for future research.

Original languageEnglish
Pages (from-to)14006-14027
Number of pages22
JournalIEEE Access
Publication statusPublished - 2023


  • assistive devices
  • Australia
  • Cerebellar Ataxia
  • Cerebellum
  • diagnoses
  • Genetics
  • Machine learning
  • machine learning
  • medical applications
  • Neurons
  • Neuroscience
  • Pathology
  • severity estimation
  • signal processing

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