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We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms in 19.5% state of the art computer vision-based approaches for body fat estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean Absolute Error (MAE) of 1.42. The study evaluates both gender-based and Gender-neutral approaches, with the latter showcasing superior performance. The method estimates BFP with 95% confidence within an error margin of 4.01% to 5.81%. This research advances multi-task learning and body composition assessment theory through ShapedNet.

This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to convey the benefits of SR as a modeling technique, we demonstrate an application within the field of health and nutrition using publicly available National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control and Prevention (CDC), fusing together anthropometric markers into a simple mathematical expression to estimate body fat percentage. We discuss the advantages and challenges associated with SR modeling and provide qualitative and quantitative analyses of the learned models.

Amazon unveiled its Halo health and fitness tracker over the summer that pairs with an AI-powered app to provide users insight into their overall wellness – but it seems to also shame wearers. The Washington Post released a report that the technology points out flaws such as an overbearing voice or too much body fat. The wearable is equipped with a tiny microphone that listens to your voice, allowing it to determine the tone and shares descriptions such as'opinionated' or'condescending.' Users can also upload a near-naked image of themselves to the companion app, which is analyzed by AI to determine their body composition. The app not only displays body fat percentage, but also shows users how they would look after shedding a few pounds.

Amazon is bringing out its first fitness tracker, a wristband called the Halo, which will eavesdrop on its owner to assess if they are happy or sad based on their tone of voice. A pair of always-on microphones will listen to users, trying to assess their mood and stress levels. After a training period, the band periodically checks in and listens to your speech, categorising it into a number of emotional buckets – such as hopeful, elated or hesitant – and offer an overall view of peak positive and negative periods. Meanwhile, a set of AI tools built into the accompanying service, which will cost $3.99 a month in the US (it is not currently available in the UK), will attempt to gauge information such as weight and body fat percentage by asking users to take undressed selfies with the app. The company says these features allow the Halo system to do more with less.