An Inertia-Based Upper Limb Motion Capture Model for Accurate Position Estimation in Magnetic Field Disturbances

Inertial-based upper limb motion capture technology is widely applied in directly controlling robots through human motions due to its low cost and portability. Most upper limb motion capture models typically focus solely on estimating the wrist position (WP) while neglecting the motion of the wrist. Moreover, these models struggle to precisely estimate joint positions, especially in the presence of magnetic disturbances. In this article, a two-layer upper limb motion capture model is proposed. In the upper layer of the model, a hand center position estimation method (HCPEM) is presented to estimate accurately hand center position (HCP) regardless of magnetic field interference. In the lower layer of the model, an optimized sensor-to-segment alignment algorithm based on physical constraints (OABPCs) is proposed to align sensors and segments with the aim of improving the precision of the estimated HCPs. The UR3 robotic experiment platform was designed to verify the accuracy of the HCPs. For different motion tasks and different mounting positions of the inertial measurement units (IMUs), the performance of the proposed two-layer model was further investigated through experiments. The results show that the HCP estimated by the proposed two-layer model achieves root-mean-square errors (RMSEs) below 2.5 cm for different motion tasks and various IMU mounting positions, even in the presence of magnetic field disturbances. Compared with the existing algorithm, the RMSE of the joint position measurements is improved by 1 cm. This study enables flexible, accurate, and convenient control of the robot through human motion.