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Detection and Severity Assessment of Parkinson’s Disease by Analysis of Wearable Sensors Data Using Gramian Angular Fields and Deep Convolutional Neural Networks

Developed a method for diagnosis and severity assessment of PD using a model based on Gramian Angular Fields in combination with deep Convolutional Neural Networks (CNNs)

Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors

A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease

AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset

We introduces a novel Transformer-based framework, AttenGluco, designed to improve long-term blood glucose level forecasting using multimodal data—including CGM and activity signals. By integrating cross-attention and multi-scale attention mechanisms, the model effectively fuses time-series data with different sampling rates and captures long-term dependencies, outperforming a multimodal LSTM baseline by up to 12% in RMSE across multiple cohorts (healthy, prediabetes, and type 2 diabetes) using the AI-READI dataset.