  Parkinson's disease (PD) is a non-curable disease that commonly found among
elders that greatly reduce their quality of life. PD primarily affects the gait
pattern and slowly changes the walking gait from the normality to disability.
The early diagnosing of PD is important for treatments and gait pattern
analysis is used as a technique to diagnose PD. The present paper has
identified the raw spatiotemporal ground reaction force (GRF) as a key
parameter to identify the changes in human gait patterns associated with PD.
The changes in GRF are identified using a convolutional neural network through
pre-processing, conversion, recognition, and performance evaluation. The
proposed algorithm is capable of identifying the severity of the PD and
distinguishing the parkinsonian gait from the healthy gait. The technique has
shown a 97% of accuracy in automatic decision-making process.
