Modeling Long-Term Human Activeness Using Recurrent Neural Networks for Biometric Data

Abstract

This paper explores the feasibility of modeling a person’s “activeness” using biometric data retrieved from a fitness tracker. Currently, the notion of activeness of a user at a given period time is defined to be a tuple of three types of biometric data: heart rate, consumed calories, and the number of steps taken. Four recurrent neural network (RNN) architectures are proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. The dataset used in this study consists of several months of biometric time series data gathered by seven users independently. The experimental results show that forecasting the users’ activeness is indeed feasible under suitable lengths of input and output sequences.