Feasibility Analysis of Wearables Guiding Scientific Movements and Promoting Health

Wearable devices have gradually integrated into people's healthy lives because of their mobility, portability, and other characteristics, and have shown their value and status in sports and health. Wearable devices can be used to capture a large amount of human body activity data, but how to effectively use these data to serve people and help people form a healthy lifestyle is a problem to be considered. In order to further study the feasibility of wearable devices to guide scientific movements and promote health, a new layered motion recognition algorithm is proposed in this study. In this study, a C4.5-based decision tree algorithm is used to identify the state layer, and only the mean and variance features are extracted from the acceleration sensor data. Three corresponding BP neural network classifiers are constructed and classified. Each classifier is responsible for identifying actions in the corresponding states and verifying the method in this study through experiments. The experimental results in this study show that the recognition rate of the mRMR feature selection recognition algorithm is 1.13% higher than the BE algorithm and 2.02% higher than the recognition method without any feature selection algorithm. In addition, the research in this article found that wearable devices can realize the real-time detection of the physiological indicators of the wearer throughout the day to evaluate the efficacy of the drug and apply it to the early detection and treatment of diseases, which may improve patient compliance and promote health to a certain extent.