A Heart Rate Prediction Model for the Telerehabilitation Training of Cardiopulmonary Patients.

Chronic obstructive pulmonary disease (COPD) and coronary artery disease are severe diseases with increasing prevalence. They cause dyspnoea, physical inactivity, skeletal muscle atrophy and are associated with high costs in health systems worldwide. Physical training has many positive effects on the health state and quality of life of these patients. Heart Rate (HR) is an important parameter that helps physicians and (tele-) rehabilitation systems to assess and control exercise training intensity and to ensure the patients’ safety during the training. On the basis of 668 training sessions (325 F, 343 M), demographic information and weather data, we created a model that predicts the training HR for these patients. To allow prediction in different use cases, we designed five application scenarios. We used a stepwise regression to build a linear model and performed a cross validation on the resulting model. The results show that age, load, gender and former HR values are important predictors, whereas weather data and blood pressure just have minor influence. The prediction accuracy varies with a median root mean square error (RMSE) of ≈11 in scenario one up to ≈3.2 in scenario four and should therefore be precise enough for the application scenarios