We propose a personalized hydration monitoring system with wearable and machine learning.
We propose a self-supervised learning (SSL) approach for cognitive workload classification using wavelet-based augmentations of EEG signals.
Our patient-independent model achieved an overall accuracy of 78% in detecting FoG events using both medication ‘On’ and ‘Off’ state data.