Deep Sense: Deep Learning for Early Staging the Onset of Diabetes and Optimizing Insulin Dosages Emanating Recognition of Activity Patterns

The main research contribution of Deep Sense is a smartphone-based, low-power framework that combines 14-class diabetes-relevant Human Activity Recognition using LSTM, GAN-based synthetic sensor-data augmentation, and cosine-similarity-based diabetes risk scoring against a clinically sourced diabetic-patient dataset. The approach achieved 98.48% test accuracy, while the risk-scoring stage produced a 57.39% similarity score that was corroborated by an A1C result of 6.1% in the evaluated subject.