Parkinson's disease is a degenerative movement disorder causing considerable disability. However, the early detection of this syndrome and of its progression rates may be decisive for the identification of appropriate therapies. For this reason, the adoption of Neural Networks to detect this disease on the base of walking information is gaining more and more interest. In this paper, we defined a Deep Neural Network based approach allowing one to exploit the information coming from various sensors located under the feet of a person. The proposed approach allows one to discriminate people affected by the Parkinson syndrome and detect the progression rates of the disease itself. To evaluate the proposed architecture we used a known dataset with the aim to compare its performance with other similar approaches. Moreover, we performed an in-depth hyper-parameter optimization to find out the best neural network configuration for the specific task. The comparison shows that the proposed classifier, trained with the best parameters, outperforms the results proviously obtained in other studies on the same dataset.

Early Detection of Parkinson Disease using Deep Neural Networks on Gait Dynamics

R. Pecori
2020

Abstract

Parkinson's disease is a degenerative movement disorder causing considerable disability. However, the early detection of this syndrome and of its progression rates may be decisive for the identification of appropriate therapies. For this reason, the adoption of Neural Networks to detect this disease on the base of walking information is gaining more and more interest. In this paper, we defined a Deep Neural Network based approach allowing one to exploit the information coming from various sensors located under the feet of a person. The proposed approach allows one to discriminate people affected by the Parkinson syndrome and detect the progression rates of the disease itself. To evaluate the proposed architecture we used a known dataset with the aim to compare its performance with other similar approaches. Moreover, we performed an in-depth hyper-parameter optimization to find out the best neural network configuration for the specific task. The comparison shows that the proposed classifier, trained with the best parameters, outperforms the results proviously obtained in other studies on the same dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/30151
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