IMU-to-Segment Calibration with Stiff Joint Using Deep Learning Integrating Kinematic Constraints

Abstract

IMU-to-Segment(I2S) calibration is a critical step in using IMU for human motion capture, as it determines the relative orientation and position between the IMU and the body segment it is attached to. Traditional constraint-based method rely on kinematic constraints to perform I2S calibration but fail in scenarios where no relative motion occurs between connected segments. To address this limitation, this thesis extends existing deep learning approaches for I2S calibration to the stiff case and investigates methods to further enhance calibration accuracy.

After completing I2S calibration using deep learning with a single IMU in stiff case, this thesis further explores joint training with dual-IMU model and integrates kinematic constraints into the model. Experiment results demonstrate that the joint training allows the models to leverage inter-IMU motion information, improving the model performance. Furthermore, integrating kinematic constraints with appropriate weights into the loss function of deep learning model improves calibration accuracy by guiding predictions to satisfy physical constraints. However, overly large constraint weights may result in larger calibration error.

This thesis provides insights into how deep learning can be adapted to address the challenges of I2S calibration in stiff joint scenarios. It also combines deep learning with kinematic information through joint training and the integration of kinematic constraints, achieving improved calibration accuracy. Future work will explore the application of this approach to real-world motion data and the integration of diverse kinematic constraints.