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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

Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals

We introduce MetaBoost, a novel hybrid framework that integrates SMOTE, ADASYN, and CTGAN, optimizing synthetic data generation through weighted averaging and iterative weight tuning to enhance the model's performance (achieving up to 1.87% accuracy improvement over individual balancing techniques).

Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification

By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.

Neonatal Risk Modeling and Prediction

We propose an automated risk prediction system that can make recommendations to clinicians in real-time with machine learning classifiers that predict the risk of developing neurological impairment.

Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design

We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.

Designing Deep Neural Networks Robust to Sensor Failure in Mobile Health Environments

We propose an algorithm that can reconstruct the missing input data for unavailable sensors.