Parkinson’s disease (PD) affects approximately 3% of people over 65 years of age worldwide, afflicting patients with symptoms such as muscle rigidity, bradykinetic gait, and severe tremor. Parkinson's disease (and similar neurodegenerative diseases) are very difficult to detect before symptoms become manifest, with some cases reporting between 25-30% of PD patients being misdiagnosed at an early stage.
As human lifespans increase due to improved quality of life, it becomes ever more important to identify and understand the mechanisms of neurodegenerative diseases in order to diagnose individuals as early as possible, so that interventions can be put in place to deal with challenges that lie ahead.
To this end, my research with the personal sensing group at Insight and the Neuromuscular Systems group in the School of Electrical and Electronic Engineering aims to develop quantitative biomarkers that can accurately detect the early onset of motor symptoms, assess the efficacy of medical interventions, and monitor patient symptoms. The core of my research uses electromyography (EMG) and accelerometry (ACC) to record muscular activity and locomotion in patients with neurodegerative diseases. By employing nonlinear signal processing methods, features of the data are extracted and analysed to provide an insight into the underlying contributing mechanisms to the neurodegenerative disease.