  Parkinson's disease (PD) has been found to affect 1 out of every 1000 people,
being more inclined towards the population above 60 years. Leveraging
wearable-systems to find accurate biomarkers for diagnosis has become the need
of the hour, especially for a neurodegenerative condition like Parkinson's.
This work aims at focusing on early-occurring, common symptoms, such as motor
and gait related parameters to arrive at a quantitative analysis on the
feasibility of an economical and a robust wearable device. A subset of the
Parkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has been
utilised for feature-selection after a thorough analysis with various Machine
Learning algorithms. Identified influential features has then been used to test
real-time data for early detection of Parkinson Syndrome, with a model accuracy
of 91.9%
