Human Body Movement Analysis And Embedded Terminal Implementation Based On Deep Learning

With the development of artificial intelligence technology and the increasing demand of consumers for smart home,the combination of deep learning technology and the control and adjustment of intelligent equipment can effectively promote the development of smart home towards the direction of active intelligence.At the same time,people are increasingly pursuing a healthy lifestyle.Exercise and fitness have become an indispensable part of modern people,and the demand for monitoring and evaluation of exercise amount is increasing.Based on the above requirements,this paper studies the application and realization of deep learning model on embedded platform with the background of human target detection and human movement analysis in indoor home environment.The main work of this paper is as follows:(1)Aiming at the problems such as large calculation amount of parameters,long training time and heavy model weight of deep convolutional neural network,a new lightweight network model MobileOne-YOLOv5-lite is constructed to improve the network for human target detection,and good target detection effect is achieved.The comparison experiment shows that the calculation amount and parameter number of MobileOne-YOLOv5-lite model are greatly reduced,the detection accuracy is up to 98.0%in GPU hardware environment,and the prediction time per unit sample is 9ms.(2)This paper proposes a new ground grid formula monocular distance measurement method based on camera.Combined with the human target detection results in the first stage,the distance is calculated according to the grid distance measurement formula corresponding to the body frame.The experimental results show that the average distance measurement error is 34.96cm in the 4m×4m area,which lays the foundation for the next stage of human exercise analysis.(3)Aiming at the problem of large-scale calculation and parameter volume of the key point extraction model of the AlphaPose algorithm,this paper designs a lightweight MobileAlphaPose network;At the same time,a dual judgment system for human body movement estimation is designed to meet the needs of human body movement analysis in irregular behaviors.Human movement behavior is divided into normative standard type and nonstandard standard type,and the amount of exercise of different action behaviors is analyzed according to the situation.For the standard behavior,MLP behavior recognition classification network is used to predict the behavior category of the key point posture and get its level of exercise.For the estimation of the amount of exercise of the non-standard behavior,a scheme is proposed to measure the amount of exercise according to the speed of human moving and the speed of the limb swing.Experimental verification shows that the detection frame rate of the lightweight version of the Mobile-AlphaPose network on the embedded platform has reached 17.8 frame/s,and the accuracy rate of behavior recognition and classification has reached 85.7%,basically meeting the detection and recognition requirements.(4)On the basis of the above research,the matching application of the algorithm model on the embedded side is completed.With Rockchip RV1126 as the processor,the system firmware production and development environment of the embedded platform are completed,and the deep learning models are transplanted to the embedded terminal for application.Through the comparison of the model running effects under different hardware environments,the balance between detection accuracy and efficiency on the embedded platform is achieved,and the effectiveness of the deep learning model in the embedded platform application is verified,reflecting its practical value.