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.

Inertial Measurement Units (IMUs) have become increasingly popular human motion estimation due to their portability, self-contained features, and cost-effectiveness compared to marker-based sensing systems which rely on external cameras to observe the position of the markers. Over the years, many studies have proposed various models and algorithms based on IMUs and the kinematics of the human body for motion estimation, neglecting the fact that IMUs measure acceleration, which can be directly related to joint torque with known inertial parameters. In turn, by estimating joint torque and incorporating kinetics into the model, it becomes possible to address a long-standing problem in the field of biomechanics: the marker-based sensing system’s inability to provide a reliable estimation of kinetics due to the need for numerical differentiation. In a previous study, a kinetics model was proposed for estimating the motion but validated only on a robotic arm. In our study, we further explore the performance of using IMUs to estimate wheelchair user motion data based on Extended Kalman Filter (EKF) and the kinetics model, with marker-based Inverse Kinematics (IK)/Inverse Dynamics (ID) as the benchmark.

Compared to Marker-based IK, the method leveraging kinetics achieves a Root Mean Squared Difference (RMSD) below 16◦ for three out of four tasks across all joints throughout the trial. After analyzing the only task with degradation in estimation, we conclude that the erroneous IMUs measurements results from Soft Tissue Artefacts (STA) is the most likely reason. For joint torque estimation, the RMSD for joint torque estimation is below 3.05Nm for the tasks less affected STA. Through fine-tuning the EKF, we can achieve fast and responsive estimation results without being affected by numerical differentiation, enabling us to capture both sudden and subtle changes in joint torque estimation. The kinetics model performs better than the kinematics-based model in estimating both kinematics and kinetics and also reduced drifting behavior. Compared to OpenSense, which depends on magnetometer measurements, kinetics model estimation shows comparable kinematics estimation accuracy while excluding
the use of heading information. The results show that including the kinetics model for human motion estimation can improve estimation accuracy and robustness encouraging further studies to include kinetics for human motion estimation.