How to Explore Apple Watch Data in R

| June 4, 2018

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This project originally appeared on Inertia7, a social platform of open source computer and data science projects. It was republished with permission from the author. Thanks to the popularity of wearable technology, data scientists and health professionals have found a mutually beneficial tool for gaining insight into a powerful data generator: the human body. Because data from wearable devices are so readily available, many organizations and researchers have taken steps towards utilizing and understanding them to improve the human experience, such as evaluating a user's health history. First, a few examples: Evidation Health has done studies which utilize longitudinal wearable technologies to predict a binary anxiety diagnosis with the help of temporal convolutional neural networks. These networks are able to perform better than baseline models because they use data collected at increasing time granularities, such as steps and sleep in minute-long intervals. Meanwhile, researchers in San Francisco have created a semi-supervised deep learning model that can predict medical conditions with the help of wearable app data from commercial offerings like FitBit and Apple Watch. By utilizing long short-term memory (LSTM),  the model yields better predictive power than baseline models (such as classification models that are not considered deep learning, including random forest and multilayer perceptron).

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