Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Abstract

Traditional health monitoring methods rely on wired transmission, which limits the flexibility and real-time data acquisition. Therefore, technology combining optical sensors and wireless mobile networks offers new opportunities for health monitoring. This study aims to explore the application of deep Convolutional neural network (DCNN) algorithm based on optical sensor and wireless mobile network in real-time health monitoring, improve the accuracy and real-time monitoring, and support personalized health management. A monitoring system integrating optical sensor and wireless mobile network is designed. Deep convolutional neural network is used to process the data collected by sensor. The system realizes real-time data transmission through the mobile network, and uploads the user’s physiological data to the cloud for analysis. During the experiment, we conducted a series of tests, including the monitoring of physiological parameters such as heart rate and blood oxygen saturation, and compared it with traditional methods. The experimental results show that the monitoring system based on DCNN has a high identification accuracy in multiple health parameters, and the application of wireless mobile network reduces the data transmission delay to the millisecond level, ensuring the real-time and effectiveness of health monitoring information. In addition, the data acquisition effect of the user in the mobile state is good, which fully demonstrates the portability and convenience of the system.