Alternative Technological Interventions for Analysis of Neurodegenerative Tremors

Parkinson's Disease (PD) and Essential Tremors (ET) are a class of chronic neurodegenerative disorders. One of the most damaging symptoms include frequent bouts of pathological tremors that renders day-to-day movement and tasks almost impossible. Manual administration of medication like Levodopa has been the primary method of providing relief for a significant amount of time, but recent advancements in biomedical sensing and machine learning are slowly helping develop a newer generation of noninvasive, real-time, and control-based remedies for tremors. Sensors that include but are not limited to gyroscopic sensors, electromyography sensors and electroencephalography sensors have been often used to provide clinical data on tremors and as such can be used for tremor rectification systems. The use of recurrent neural network algorithms trained on time-series data from PD and ET patients are being used to discern features that indicate onset of tremors and even predict what the waveform will look like in the future. There are now attempts being made to significantly, if not completely remove resting and essential tremors. This class of devices, the main methodologies of their development, feasible sensing, data analysis and remediation methods, and the future development of wearable devices and overall quality-of-life improvements of patient are discussed in these proceedings.