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This paper focuses on the detection of Parkinson's disease based on theanalysis of a patient's gait. The growing popularity and success of Transformernetworks in natural language processing and image recognition motivated us todevelop a novel method for this problem based on an automatic featuresextraction via Transformers. The use of Transformers in 1D signal is not reallywidespread yet, but we show in this paper that they are effective in extractingrelevant features from 1D signals. As Transformers require a lot of memory, wedecoupled temporal and spatial information to make the model smaller. Ourarchitecture used temporal Transformers, dimension reduction layers to reducethe dimension of the data, a spatial Transformer, two fully connected layersand an output layer for the final prediction. Our model outperforms the currentstate-of-the-art algorithm with 95.2\% accuracy in distinguishing aParkinsonian patient from a healthy one on the Physionet dataset. A keylearning from this work is that Transformers allow for greater stability inresults. The source code and pre-trained models are released inhttps://github.com/DucMinhDimitriNguyen/Transformers-for-1D-signals-in-Parkinson-s-disease-detection-from-gait.git

Transformers for 1D Signals in Parkinson's Disease Detection from Gait