Abstract
The position calibration of inertial measurement units (IMUs) is an important part of human motion capture, especially in wearable systems. In realistic applications, static calibration is quickly invalid during the motions for IMUs loosely mounted on the body. In this paper, we propose a dynamic position calibration algorithm for IMUs mounted on the waist, upper leg, lower leg, and foot based on joint constraints. To solve the problem of IMUs’ position displacement, we introduce the Gauss–Newton (GN) method based on the Jacobian matrix, the dynamic weight particle swarm optimization (DWPSO), and the grey wolf optimizer (GWO) to realize IMUs’ position calibration. Furthermore, we establish the coordinate system of human lower limbs to estimate each joint angle and use the fusion algorithm in the field of quaternions to improve the attitude calibration performance of a single IMU. The performances of these three algorithms are analyzed and evaluated by gait tests on the human body and comparisons with a high-precision IMU-Mocap reference device. The simulation results show that the three algorithms can effectively calibrate the IMU’s position for human lower limbs. Additionally, when the degree of freedom (DOF) of a certain dimension is limited, the performances of the DWPSO and GWO may be better than GN, when the joint changes sufficiently, the performances of the three are close. The results confirm that the dynamic calibration algorithm based on joint constraints can effectively reduce the position offset errors of IMUs on upper or lower limbs in practical applications.
Highlights
In recent years, inertial measurement units (IMUs) have attracted increasing interest in the field of human motion analysis
When the grey wolf optimizer (GWO) is used for position calibration, the initial population is easy to be unevenly distributed and lacks global communication, resulting in the final solution being easy to fall into local optimization
We introduce the dynamic weight particle swarm optimization (DWPSO), GWO, and GN algorithms to realize the dynamic calibration of IMUs’ positions based on human lower limb joint constraints
Summary
Inertial measurement units (IMUs) have attracted increasing interest in the field of human motion analysis. By mounting IMUs on each limb of human bodies, the real-time tracking and motion data analysis of human postures can be realized. The IMU-based motion capture and analysis have shown substantial applications in athletic training, e.g., golf training, baseball training, dart-throwing training, etc. To obtain the information of body motions, the installation position of the IMUs and the variation of joint space position should be accurately measured. In practical applications, different types of clothing materials and muscle stretching during exercise will cause IMU position displacements. A direct consequence of IMU displacement is the difference of the derived joint position relative to the pre-calibration.
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