Abstract
Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing disease-modifying therapy. The premotor stage in PD should be prudently checked for the early detection of PD. An innovative deep-learning technique is introduced to quickly uncover whether an individual is in the mild motor impairment stage of Parkinson's disease. The mild motor impairment stage is the pre-motor stage of Parkinson's disease. The pre-motor stage of Parkinson's disease is detected by applying an end-to-end deep learning model. Implementing and optimizing the algorithm of an embedded platform is crucial. Literature shows that NVIDIA Jetson is an exemplar embedded system; many of the ideas and optimizations will apply just as well to existing and future embedded systems. It is widely believed that the ability to run AI algorithms on low-cost, low-power platforms will be crucial for achieving advances in biomedical engineering.
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