Predicting the Heart Rate Response to Outdoor Running Exercise

Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.

[1]  Manolis I. A. Lourakis.  A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar , 2005 .

[2]  Kent Larson,et al.  Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[3]  Andrey V. Savkin,et al.  Modeling of human Heart Rate response during walking, cycling and rowing , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[4]  Lu Wang,et al.  A nonlinear dynamic model for heart rate response to treadmill walking exercise , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Henri P. Gavin,et al.  The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c © , 2013 .

[6]  Lu Wang,et al.  Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression , 2009, Physiological measurement.

[7]  Ming Yuchi,et al.  Heart Rate Prediction Based on Physical Activity Using Feedforwad Neural Network , 2008, 2008 International Conference on Convergence and Hybrid Information Technology.

[8]  Andrey V. Savkin,et al.  Nonlinear Modeling and Control of Human Heart Rate Response During Exercise With Various Work Load Intensities , 2008, IEEE Transactions on Biomedical Engineering.

[9]  Keiichi Yasumoto,et al.  Estimating heart rate variation during walking with smartphone , 2013, UbiComp.

[10]  William H. Press,et al.  Numerical recipes in C , 2002 .

[11]  Cristiano Maria Verrelli,et al.  Experimental Heart Rate Regulation in Cycle-Ergometer Exercises , 2013, IEEE Transactions on Biomedical Engineering.