An innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer
We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.
This paper revisits Deep Geometric Moments (DGM), a framework for learning shape-aware, geometry-aligned visual representations, by swapping the original ResNet backbone for ConvNeXt, a modern high-performing ConvNet. Using ConvNeXt feature maps as the input “stem” for DGM, the authors show consistent ImageNet-1K accuracy gains over a ResNet34-DGM baseline (up to ~+2.4% depending on ConvNeXt size) while preserving and strengthening DGM’s shape bias.