Machine Learning-based Approach for Predicting Health Information Using Smartwatch Data

This paper presents a machine learning-based method for predicting health information in terms of heart rate, sleep quality, burned calories and performed physical activity for a person wearing a smartwatch. The proposed method consists of three main steps, namely, data pre-processing, feature selection, and training and testing. Based on a data set collected with a Fitbit Versa 2 smartwatch and by applying three different machine learning algorithms (i.e. Long Short-Term Memory, Multilayer Perceptron, and Back propagation from scratch) the method is able to predict the efficiency of the sleep, the number of burned calories, the average heart rate and the name of the activity performed by a person. To evaluate the experimental results the accuracy score and logarithmic loss metrics have been used.