Parkinson’s Disease is a movement disorder characterized by the degeneration of dopaminergic neurons. The patients experience a range of motor symptoms such as rest tremors, slowness in movement, muscle rigidity, and postural instability. As a result, difficulty in starting and stopping movements has been observed in such patients. These variations in movements can be tracked from his utterance, handwriting, and walking style. The start/stop portions of these actions can be viewed as a transition between voiced and unvoiced parts of speech, as the small pause in writing in between words that marks new strokes and walking from rest and stopping after walking. Though unimodal approaches have shown considerable progress in the detection of Parkinson’s disease, combining additional modalities as aforesaid for this task has its own potential for improvements. Hence, the present study proposes utilizing a multimodal approach that can effectively capture the difficulty in transition between tasks as a basis to effectively detect Parkinson’s Disease.
Center for Computational Engineering and Networking (CEN), Coimbatore Campus
Mathematical Knowledge (Linear algebra, probability, optimization) and Programming skills, Knowledge in AI, Machine Learning and Deep Learning
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