Search Results for author:
Found 5 papers, 2 papers with code
Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning
Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations.
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
To solve the problem of limited availability of high quality training data, we propose a transfer learning technique which helps to improve model performance substantially.
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease
The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation.
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements.
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training.
