Abstract

Parkinson’s disease is a progressive neurodegenerative disorder which is characterised by cardinal motor symptoms including resting tremor, rigidity and akinesia. Traditionally, motor symptoms have been assessed and tracked using clinical rating scales which are currently considered the gold standard. However, these scales are subject to inter-rater variability and ceiling/ floor effects. Due to advances in technology, it is now possible to use a range of sensors across a patient’s body to quantify many postural features both at home and in clinic. In this study, we used data collected from a six-sensor array during a quiet stance task to quantify postural sway features. We carried out a process of dimensionality reduction and statistical analysis to identify postural sway features which show changes between healthy controls and groups of Parkinsonian patients of differing disease states. We also conducted a levodopa challenge to assess how levodopa affects postural sway features. By identifying features that show significant changes in Parkinson’s disease, and are affected by levodopa, this could potentially lead to identification of biomarkers which could allow for earlier diagnosis, aid the progression of clinical trials for novel therapies and improve the quality of life for patients.