A Human Posture Recognition Method Based On Electric Field Induction

With the rapid development of computer vision,human gesture recognition as an important part of computer vision has become more and more loved by researchers.Human posture refers to the state in which a person’s head,limbs,and trunk are in coordinated movement at a specific time or time period.Through the recognition of human posture,users can more easily make the human posture recognition system get its true ideas,and then for Users provide more convenient and user-friendly services,which also makes human gesture recognition have many applications in the fields of advanced human-computer interaction,game entertainment,smart medical treatment,film and television production,etc.,and how to make the computer recognize human bodies more quickly and accurately Gestures have become the focus of research by scientists in the field of computer vision.The existing human body gesture recognition technology is difficult to accurately recognize the situation where the detected target is blocked,and at the same time,a large amount of calculation is required for human body gesture recognition.Regarding the problems in the existing human body pose recognition technology,this paper proposes a human body pose recognition method based on electric field induction.This method has a small calculation amount and is not affected by obstacles.First,the two-channel detection model is introduced,and the effects of the detection target distance from the detection electrode spacing,the detection electrode spacing,the detection target movement speed,and the detection electrode size on the signal characteristic map are discussed respectively.The feasibility of recognizing human posture;secondly,a human posture recognition system was built.The frontend detection circuit system short-circuit noise test and amplitude-frequency characteristic test showed that the system has the characteristics of high sensitivity and low noise.Design the host computer to collect the detection signal,which is convenient and practical.Finally,the KNN classification algorithm is used to train the collected 5034 sets of data,which can effectively identify the gestures of waving,raising legs,walking,and falling.The system recognition accuracy rate is 96.5%.The feasibility of the proposed method is illustrated.