Research on a personalized physiological status cluster discrimination system based on pulse signal

Abstract

At present, the workload of mental workers in society is getting heavier and heavier, and it is necessary to conduct research on their physiological state assessment. Considering that the pulse is easily available and non-invasive, the pulse wave signal is used as the data source. The infrared pulse sensor is used to collect the pulse wave signals of the human body in different states, and the data is preprocessed and feature extraction is performed, and then the feature value weight is calculated by the Relief algorithm to obtain a new weighted pulse sample. The third step is to use k-means algorithm and fuzzy C-means (FCM) algorithm for clustering analysis of the weighted eigenvalue samples, and use the three parameters of error square sum, contour coefficient, and CH coefficient as clustering effect evaluation indicators. Finally, The state of the clustered model is determined according to specific eigenvalues, and the state classification is realized. Thus, a personalized physiological state classification method is proposed, which is to establish different classification algorithm models for different individuals, thereby avoiding Individual differences.