Exercise prescription recommendation method for hypertensive old people based on deep learning

TLDR: In this article, an exercise prescription recommendation method for the for hypertensive old people based on deep learning was proposed, which comprises the steps: collecting health data; testing the cardiac function state of the hypertensive older people in a quiet state, and evaluating the heart rate, the blood pressure and the variability of heart rate variability; identifying daily actions of the elderly people; evaluating the cardiopulmonary function of the old people; and making personalized exercise prescriptions by referring to FITT rules according to BMI, age, clinical diagnosis and exercise preference.

Abstract: The invention discloses an exercise prescription recommendation method for the for hypertensive old people based on deep learning. The method comprises the steps: collecting health data; testing the cardiac function state of the hypertensive old people in a quiet state, and evaluating the heart rate, the blood pressure and the heart rate variability; identifying daily actions of the hypertensive old people; evaluating the cardiopulmonary function of the hypertensive old people by combining heart rate variability, energy consumption and heart rate according to the exercise-blood pressure risk grade and exercise risk contribution degree, and making personalized exercise prescriptions for the old people by referring to FITT rules according to BMI, age, clinical diagnosis and exercise preference. According to the deep learning scheme provided by the invention, the characteristic representation of exercise parameters such as exercise modes and intensity and time series data such as heart rate, electrocardiogram and real-time blood pressure is better extracted, the relation between exercise and hypertension fluctuation rules is fully learned and mined in combination with population informatics, and a personalized exercise prescription is generated.