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Diabetes impacts the quality of life of millions of people. However, diabetesdiagnosis is still an arduous process, given that the disease develops and getstreated outside the clinic. The emergence of wearable medical sensors (WMSs)and machine learning points to a way forward to address this challenge. WMSsenable a continuous mechanism to collect and analyze physiological signals.However, disease diagnosis based on WMS data and its effective deployment onresource-constrained edge devices remain challenging due to inefficient featureextraction and vast computation cost. In this work, we propose a frameworkcalled DiabDeep that combines efficient neural networks (called DiabNNs) withWMSs for pervasive diabetes diagnosis. DiabDeep bypasses the feature extractionstage and acts directly on WMS data. It enables both an (i) accurate inferenceon the server, e.g., a desktop, and (ii) efficient inference on an edge device,e.g., a smartphone, based on varying design goals and resource budgets. On theserver, we stack sparsely connected layers to deliver high accuracy. On theedge, we use a hidden-layer long short-term memory based recurrent layer to cutdown on computation and storage. At the core of DiabDeep lies a grow-and-prunetraining flow: it leverages gradient-based growth and magnitude-based pruningalgorithms to learn both weights and connections for DiabNNs. We demonstratethe effectiveness of DiabDeep through analyzing data from 52 participants. Forserver (edge) side inference, we achieve a 96.3% (95.3%) accuracy inclassifying diabetics against healthy individuals, and a 95.7% (94.6%) accuracyin distinguishing among type-1/type-2 diabetic, and healthy individuals.Against conventional baselines, DiabNNs achieve higher accuracy, while reducingthe model size (FLOPs) by up to 454.5x (8.9x). Therefore, the system can beviewed as pervasive and efficient, yet very accurate.

DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks