A Unified Kinematic Representation Enables Reusable Biological Joint Moment Estimation

2026-10-06

AI Summary

This study addresses the strong coupling between biological joint torque estimators and hardware-specific sensors in exoskeleton control, which hinders cross-device transferability and precludes the utilization of open-source data. To overcome this limitation, we propose a generalizable framework that decouples sensing from estimation by employing joint kinematics—specifically angles and angular velocities—as an intermediate representation. This approach innovatively leverages open-source biomechanical datasets to train machine learning-based torque estimators, thereby eliminating dependence on device-specific training data. Validated across multiple platforms, including hip and knee exoskeletons as well as inertial measurement units (IMUs), the proposed method achieves highly accurate cross-device torque estimation, yielding a root mean square error as low as 0.15 Nm/kg and an R² of 0.85. These results demonstrate a reusable, generalizable solution for wearable device control.

Abstract

Objective: Data-driven models that estimate physiological states, particularly biological joint moments, are widely used in exoskeleton control. However, these estimators are often coupled to device-specific sensor configurations, limiting controller transfer and the use of open-source biomechanics datasets. Methods: We proposed joint kinematics as an intermediate representation that decouples hardware-specific sensing from downstream biological joint moment estimation. A joint-moment estimator using joint angles and angular velocities was trained exclusively on open-source biomechanics data and evaluated using kinematics from a hip exoskeleton, knee exoskeleton, and inertial measurement unit (IMU) sensor suite during level-ground, ramp-ascent, and ramp-descent walking. Results: The estimator achieved an root mean square error (RMSE) of 0.17 Nm/kg and coefficient of determination (R2) of 0.79 using hip exoskeleton kinematics, 0.19 Nm/kg and 0.62 using knee exoskeleton kinematics, and 0.15 Nm/kg and 0.85 using kinematics derived from IMUs across bilateral hip, knee, and ankle joints. Conclusion: Joint kinematics enabled an estimator trained only on open-source data to operate across distinct wearable platforms. Significance: This framework may reduce target-device data collection and support transferable biological joint moment estimation for exoskeleton control and wearable biomechanics.

Problem

• joint moment estimation
• exoskeleton control
• sensor coupling
• controller transfer
• wearable biomechanics

Innovation

• Joint Kinematics
• Biological Joint Moment Estimation
• Exoskeleton Control
• Intermediate Representation
• Transferable Estimation