Heart Rate Prediction Based on Physical Activity using Feedforward Neural Network

Abstract

The technique of combining heart rate (HR) and physical activity (PA) has been adopted in a number of research areas, such as energy expenditure measurement, autonomic nervous system assessment, sports research, etc. However, there have been few studies on the direct relationship between HR and PA. This paper proposes a HR prediction model based on the relationship between HR and PA. The predictor has the potential to be used in various areas, such as: cardiopathy research and diagnosis, heart attack warning indicator, sports capability measure and mental activity evaluation. The method has the following steps: first, the recorded HR and PA signals are preprocessed as two synchronized time sequences: HR(n) and PA(n). The inputs of the predictor are HR(n) and PA(n) in the current time step, and the output is the predicted sequence HR(n + 1) in the next time step. The feed forward neural network (FFNN) was chosen as the mathematical model of the predictor. Experiments was conducted based on the real-life signals from a healthy male. A set of 90 minute signals were collected. One half of the signal set was used to train the FFNN and the other half to validate the training. The mean absolute error of the predicted heart rate was restricted inside 5. The result shows the potential of the proposed method.