O'Callaghan, BenBenO'Callaghan2022-09-282022-09-282022 the A2022http://hdl.handle.net/10197/13142Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. Increasing global populations and life expectancies are resulting in greater numbers of individuals impacted by various neurological disorders. A wide variety of treatment options are currently available, however, due to restrictions in clinical time, subjective rating of motor function, inter-patient variability and daily fluctuations of motor symptoms, the optimal treatment is often difficult to achieve. Prompted by these limitations, there is growing interest in the use of wearable sensors for the quantification of movement and to improve the understanding of the pathological physiological processes that impair motor function. The development and application of signal processing based algorithms for the quantification of movement and motor control strategies in neurological disorders could further advance the clinical understanding of the changes that occur throughout disease progression as well as to provide quantitative measures of treatment efficacy. The focus of this thesis is therefore to explore the application of signal processing methods to surface electromyography (sEMG) and accelerometery (ACC) to quantify the effects of therapeutic interventions for neurological disorders, namely Parkinson’s Disease (PD) and stroke. In conclusion, the findings in this thesis demonstrate the ability for wearable sensing technologies to provide clinically relevant information for the assessment of therapeutic interventions as well as illustrating their ability to be used as biomarkers in the design and advancement of therapeutic devices for the rehabilitation and treatment of neurological disorders. Since the sudden onset of the COVID-19 pandemic, there is a substantially increased need for remote healthcare solutions, particularly those providing quantitative clinical measures. This thesis demonstrates the ability for wearable sensors to quantify the effects of therapeutic interventions and may help to reduce the current barriers that have so far limited the widespread uptake of quantitative sensors in the clinic.enElectromyographyAccelerometerySignal processingNeurological disordersDoctoral Thesis2022-09-20https://creativecommons.org/licenses/by-nc-nd/3.0/ie/