Journal
SENSORS
Volume 20, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s20092605
Keywords
Parkinson's disease; sEMG; DCGAN; style transfer; signal processing
Funding
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2020/06249-4]
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This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient's tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.
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