Joint kinematics power joint moment estimates across exoskeletons and sensors
A Unified Kinematic Representation Enables Reusable Biological Joint Moment Estimation
Robotics
Summary
Estimating how much force our leg joints generate is important for controlling wearable machines like exoskeletons. Usually, these estimates depend heavily on the specific sensors used with each device, making it hard to reuse data. The authors found that by focusing on measurements of joint angles and speeds, they could train a single model on open data and use it successfully for different exoskeletons and sensor setups. This means fewer new data collections are needed, and estimates work more broadly across devices.
What this means in practice
- •For exoskeleton developers: Create joint moment controllers that can work across different hardware without collecting new device-specific data.
- •For wearable biomechanics teams: Use open-source biomechanics data to build joint moment estimation models that apply to varied sensor setups on the body.
Authors
Jinwoo Hwang, Ilseung Park, Changseob Song, Vu Phan, Eni Halilaj, Inseung Kang
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.