Human body posture recognition method based on self-adaptive extension Kalman filtering

Abstract

The invention discloses a human body posture recognition method based on self-adaptive extension Kalman filtering, belonging to the field of body-area networks. The method comprises a model design step and a parameter design step. In the model design step, the angular speed and accelerated speed of the motion of a human body and the peripheral magnetic field intensity are collected by virtue of an inertial sensor through the characteristic that the motion angle of limbs of the human body can be reflected by a quaternion, and posture resolving is carried out based on a self-adaptive extension Kalman filtering method so as to obtain a posture quaternion. In the parameter design step, by utilizing a theoretical analysis and experiment method, a process noise covariance matrix is determined, and the value of a noise covariance matrix, a state initial value and an initial value of a state covariance matrix are measured, so that the continuous iteration of the self-adaptive extension Kalman filtering method can be realized, and the motion posture of the human body is continuously recognized in real time. The human body posture recognition method can be used as a human body posture recognition method in the fields of physical training, medical care, game design and the like.

Description

technical field [0001] The invention belongs to the field of body area network, and relates to a human body gesture recognition method based on adaptive extended Kalman filtering. Background technique [0002] Human posture recognition can capture the spatial motion information of the human body at a certain moment or calculate the subtle deformation of the face, limbs, and torso. Has broad application prospects. There are several common methods of gesture recognition, such as optical, electromagnetic, acoustic and micro-electromechanical. Optical gesture recognition is one of the research contents of computer vision. The basic idea is to track and monitor the specific light points of the target, and then extract the moving parts of the human body in the video image sequence. The electromagnetic attitude recognition method uses the principle of electromagnetic induction. The electromagnetic emission source generates a regularly changing electromagnetic field in space. The ...

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Problems solved by technology

[0063] Since the 1960s, many physical processes have been described by nonlinear models. Kalman filtering is suitable for dealing with the state estimation of linear systems under Gaussian noise, but it is no longer applicable to nonlinear systems.

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