Research on Model for Vital Signs Estimation Based on Convolutional Neural Network

This paper introduces a model for vital signs estimation, mainly, the heart rate, breathing rate and body movement, based on convolutional neural network (CNN). Due to the limitation of hardware acquisition system and detection method, the traditional measurement method with ballistocardiogram(BCG) signals or electrocardiogram(ECG) signals is difficult to meet the requirements of high accuracy and use convenience at the same time. Aiming at this problem, this paper, which uses high precision BCG data as label value, improves the data forecast model, including the use of deep learning approach for data prediction, and the appropriate pretreatment process data, using piezoelectric ceramic signal, which records the BCG signals, to carry on the forecast, building a forecasting model for vital signs using CNN. Low complexity and good precision of the model, making it accessible for both the convenience and data accuracy of the entire vital signs monitoring system.