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Found 7 papers, 0 papers with code
Generative models for wearables data
Data scarcity is a common obstacle in medical research due to the high costs associated with data collection and the complexity of gaining access to and utilizing data.
Towards Industrial Private AI: A two-tier framework for data and model security
We proposed a three-layer encryption method for data security and provide a hypothetical method to secure the model parameters.
Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.
Adversarial Examples for Electrocardiograms
For this model, we utilized a new technique to generate smoothed examples to produce signals that are 1) indistinguishable to cardiologists from the original examples and 2) incorrectly classified by the neural network.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data
We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions.
Spatial Coherence of Oriented White Matter Microstructure: Applications to White Matter Regions Associated with Genetic Similarity
Furthermore, we show that the orientation similarity within these regions generalizes to a subset of 47 pairs of non-twin siblings, and show that these siblings are on average as similar as dizygotic twins.
Intra-day Activity Better Predicts Chronic Conditions
In this work we investigate intra-day patterns of activity on a population of 7, 261 users of mobile health wearable devices and apps.
