  Diabetes impacts the quality of life of millions of people. However, diabetes
diagnosis is still an arduous process, given that the disease develops and gets
treated outside the clinic. The emergence of wearable medical sensors (WMSs)
and machine learning points to a way forward to address this challenge. WMSs
enable a continuous mechanism to collect and analyze physiological signals.
However, disease diagnosis based on WMS data and its effective deployment on
resource-constrained edge devices remain challenging due to inefficient feature
extraction and vast computation cost. In this work, we propose a framework
called DiabDeep that combines efficient neural networks (called DiabNNs) with
WMSs for pervasive diabetes diagnosis. DiabDeep bypasses the feature extraction
stage and acts directly on WMS data. It enables both an (i) accurate inference
on 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 the
server, we stack sparsely connected layers to deliver high accuracy. On the
edge, we use a hidden-layer long short-term memory based recurrent layer to cut
down on computation and storage. At the core of DiabDeep lies a grow-and-prune
training flow: it leverages gradient-based growth and magnitude-based pruning
algorithms to learn both weights and connections for DiabNNs. We demonstrate
the effectiveness of DiabDeep through analyzing data from 52 participants. For
server (edge) side inference, we achieve a 96.3% (95.3%) accuracy in
classifying diabetics against healthy individuals, and a 95.7% (94.6%) accuracy
in distinguishing among type-1/type-2 diabetic, and healthy individuals.
Against conventional baselines, DiabNNs achieve higher accuracy, while reducing
the model size (FLOPs) by up to 454.5x (8.9x). Therefore, the system can be
viewed as pervasive and efficient, yet very accurate.
