  Diagnosing Parkinson's disease is a complex task that requires the evaluation
of several motor and non-motor symptoms. During diagnosis, gait abnormalities
are among the important symptoms that physicians should consider. However, gait
evaluation is challenging and relies on the expertise and subjectivity of
clinicians. In this context, the use of an intelligent gait analysis algorithm
may assist physicians in order to facilitate the diagnosis process. This paper
proposes a novel intelligent Parkinson detection system based on deep learning
techniques to analyze gait information. We used 1D convolutional neural network
(1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed
model processes 18 1D-signals coming from foot sensors measuring the vertical
ground reaction force (VGRF). The first part of the network consists of 18
parallel 1D-Convnet corresponding to system inputs. The second part is a fully
connected network that connects the concatenated outputs of the 1D-Convnets to
obtain a final classification. We tested our algorithm in Parkinson's detection
and in the prediction of the severity of the disease with the Unified
Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high
efficiency of the proposed method in the detection of Parkinson disease based
on gait data. The proposed algorithm achieved an accuracy of 98.7 %. To our
knowledge, this is the state-of-the-start performance in Parkinson's gait
recognition. Furthermore, we achieved an accuracy of 85.3 % in Parkinson's
severity prediction. To the best of our knowledge, this is the first algorithm
to perform a severity prediction based on the UPDRS. Our results show that the
model is able to learn intrinsic characteristics from gait data and to
generalize to unseen subjects, which could be helpful in a clinical diagnosis.
