A Mobile Health Platform for Automated Diet Monitoring Using Continuous Glucose Monitors and Context-Aware Machine Learning

Automated diet monitoring, an important tool in preventing healthy individuals and those with pre-diabetes from developing Type 2 Diabetes, requires automatic eating detection and estimation of the macronutrient contents of ingested food. While signals from continuous glucose monitors may track the post-prandial glucose response (glucose response after eating) and use this for estimation of nutritional information, the proper identification and segmentation of these periods of eating require additional sensing modalities and contextual information. In this work, we developed a framework for machine learning modeling to detect eating periods, properly segment post-prandial glucose responses, and estimate nutritional content from these segments in real-world environments using data captured from a continuous glucose monitor and augmented with con-textual data from smartwatch wearable sensors. Using a custom-developed platform, we conduct a human subject study where participants were free to eat what they wished, when they wished, logging data and wearing a set of sensors. To aid future, just-in-time diet monitoring applications, we found that contextual data improved eating moment detection and thus enables real-time macronutrient estimation

Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers?

Robust calibration of hydrologic models is critical for simulating water resource components; however, the time-consuming process of calibration sometimes impedes the accurate parameters’ estimation. The present study compares the performance of two approaches applied to overcome the computational costs of automatic calibration of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model constructed for the Tamar basin located in northern Iran. The model is calibrated using the Particle Swarm Optimization (PSO) algorithm. In the first approach, a machine learning algorithm, i.e., Artificial Neural Network (ANN) was trained to act as a surrogate for the original HMS (ANN-PSO), while in the latter, the computational tasks were distributed among different processors. Due to inefficacy of preliminary ANN-PSO, an efficient adaptive technique was employed to boost training and accelerate the convergence of optimization. We found that both approaches were helpful in improving computational efficiency. For jointly-events calibrations schemes, meta-models outperformed parallelization due to effective exploration of calibration space, where parallel processing was not practical owing to the time required for data sharing and collecting among many clients. Model approximation using meta-models becomes highly complex, particularly in the presence of combining more events, because larger numbers of samples and much longer training times are required

Barotropic to baroclinic energy conversion using a time-varying background density

Internal wave generation is fundamentally the conversion of barotropic to baroclinic energy that often occurs due to vertical acceleration of stratified flows over topographic features. Acceleration results in a phase lag between density (pressure) perturbations and the barotropic velocity. To estimate the conversion of barotropic to baroclinic energy, the density perturbation is often calculated using a time-invariant background density. Other phenomena, however, can also alter the phasing of density perturbations and vertical velocities, such as barotropic tidal heaving and internal wave interactions. Consequently, accurately accounting for these dynamics in energy budgets is important. Tidal averaging or modal decomposition are often used to isolate topographic energy conversion in the presence of these other phenomena. However, while effective, these methods do not provide insights into the dynamics of conversion either through time or over depth. Here, we present a new analytical approach to calculating barotropic to baroclinic conversion using a time-varying background density. Our method results in an additional term in the baroclinic energy budget that directly accounts for barotropic tidal heaving and internal wave interactions, depending on the formulation of the background density. The tidally averaged, domain-integrated conversion rate is consistent across methods. Isolation of topographic conversion demonstrates that conversion due to interactions between internal wave beams and barotropic tidal heaving lead to relatively small differences in the overall conversion. However, using a time-varying background density allows for full decomposition of barotropic to baroclinic conversion through time and the identification of regions where negative conversion related to mixing actually occurs