Monitoring physiological status during mid‐ and long‐distance running for smart healthcare

Abstract

The whole of society faces with the problems of sub‐health and population aging. Every person has concerned how to get rid of sub‐health status. It is particularly important to prevent and cure disease for everyone. Monitoring physiological status becomes extremely important to accurately assess health status. In this paper, we establish a system to monitor the physiological status during mid‐ and long‐distance running. The mid‐ and long‐distance running is a common exercise and easy to practice. In order to monitor physiological status, we first collect photoplethysmogram signals, blood pressure, and temperature by using wearable devices. For photoplethysmogram signals, a fast median filtering algorithm is used to remove the noises. The denoised signals are extracted 11 features. Then, the 11 extracted features from photoplethysmogram signals, blood pressure, and temperature are input into a weighted one‐class support vector machine (WOC‐SVM) for training. The abnormal status can be identified 86.4% by the trained WOC‐SVM model.