Deep learning-based quantitative assessment of Parkinson's motor symptoms

Parkinson's disease, prevalent among middle-aged and elderly individuals[1], is a degenerative neurological condition characterized by symptoms such as bradykinesia, tremors in the extremities, and overall body rigidity[2]. As the population ages, there will be more and more cases[3].This disease develops from a complex interaction of genetics and environment[4].The accurate quantification of tremors is crucial for the management of this disease. However, tremor amplitude can be influenced by various factors, including environmental and psychological impacts, leading to erratic fluctuations. In clinical settings, physicians are tasked with rapidly evaluating tremor severity, often relying on their clinical expertise for manual labeling. The ability to effectively quantify the severity of Parkinson's tremors is vital for elderly patients and their caregivers. This paper addresses the existing limitations and employs suitable strategies to investigate the automated quantitative assessment of Parkinson's tremor.