Human joint pose estimation based on the Kalman filter

Abstract

With the development of Internet of Things (IoT) and artificial intelligence, people’s demand for location-based services is increasingly urgent. Aim at recognize human lower limb posture accurately, posture recognition based on image information and sensor information such as magnetometer, accelerometer, and rate gyro, MARG sensors, has been the research hot spot. However, the image methods require higher computing resources and will be affected by environmental factors such as fiber optics, shading, etc; the sensor methods have low accuracy with traditional arithmetic. Kalman filter and Attitude and Heading Reference System (AHRS) is used to extract accurate posture information from sensors’ raw data. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrating circuit (IC) of MARG sensors and a ZigBee wireless communication module called E18-MS1-PCB.