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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)

Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation

This work introduces Deep Geometric Moments (DGM) as a novel, training-free guidance mechanism for text-to-image diffusion models. Unlike existing guidance techniques (e.g., segmentation maps, depth maps, or CLIP features), which impose rigid spatial constraints or rely heavily on global semantics, DGM captures fine-grained, subject-specific visual features through robust geometric representations. The proposed method uses a pretrained DGM model during the diffusion process to steer image generation in a flexible yet identity-preserving manner. Experiments show that DGM achieves a better balance between control and diversity, enabling more nuanced and visually consistent image synthesis without retraining the diffusion model.

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