Future Heart Rate Forecast for Runners Using Wearable Derived Health and Training Data

As more and more people are using wearable devices for tracking their sports activities, the amount of data generated by these devices is growing. Such wearables are worn day to day, tracking the sleep quality, stress level, and other measurements about the wellness of the user. In addition, some wearables are optimized for sports activities, allowing the user to track his activity sessions, and measure how his performance and results change with time. As the data about the training sessions is saved, the user could potentially use it not only to track his progress, but also to personalize his training program to train effectively. As of now different machine learning and neural network models are used for forecasting and predicting values in many fields, for example, in energetics, finance. In our paper, we propose how data collected from a wearable can be used to forecast heart rate during a running session using a sequence to sequence approach. The data from a future hypothetical training can be used to forecast the heart rate curve during the activity, allowing the user to plan the intensity of the training and the route taken, therefore personalizing his training process for better results. Furthermore, the proposed approach takes into account historical well-being data collected from the wearable, that could potentially increase the prediction quality. Our experiments with several forecasting models demonstrate how this approach could be carried out, while also showcasing how different neural network models perform on such a task. In addition, we also prove, that wellness data collected during the previous day, namely, hours slept, steps taken, calories burned and others can reduce the prediction error, therefore showing that it is important to take into account such features when creating forecasts for heart rate during the running session.