In this paper, we present a Deep Learning architecture, exploiting a fuzzy layer, applied to the data coming from various sensors located under the feet of a patient affected by the Parkinson's disease. The solution we propose permits one to cluster data coming from different sensors into different fuzzy partitions, according to the different parts of the feet, and to discriminate the illness of a person as well as the severity degree of the disease itself. We employed a known dataset to evaluate our solution and compared its performance with some similar approaches found in the relevant literature. Moreover, we performed an intensive parameter optimization step to find the best setting for the proposed fuzzy neural network. The evaluation shows that our solution obtains good classification results both in the binary and in the multiclassification approach.
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