Design and implementation of infant crying monitoring and analysis system based on deep learning

Aiming at the problems that the design functions of intelligent baby monitoring products on the market are too single, such as only equipped with basic environmental monitoring functions and low accuracy of crying recognition caused by only sound sensors for baby cry monitoring, a baby monitoring system based on deep learning and multi-sensor combination is designed. The system uses Jetson Nano B01 as the model deployment terminal, combined with the single-chip microcomputer equipped with sensors and K210 visual processing unit to realize the functions of infant crying monitoring, environment and sign data detection, and based on neural network design. The function of emotional analysis of infant crying is expected to ' translate ' the potential needs and health signals of infant crying. Finally, through the analysis of the algorithm data and the actual test of the system, it is shown that the system can meet the design requirements and achieve the target function, which better solves the shortcomings of the current intelligent infant monitoring products and improves the intelligent level of infant monitoring products.