Center of Mass Feedback for joint torque control: a generalizable approach for balance augmentation in wearable robots.

Background: Exoskeletons have the potential to augment balance and decrease fall risk. However, existing balance-augmenting wearable robot controllers have only been tested in single planes of motion during either standing or walking. Thus, it is unclear whether a single control scheme can generalize across perturbations with varying spatial properties or from standing to walking. Inspired by the nervous system's generalizable balance control strategy across perturbation types and conditions, we propose a novel torque control framework that modulates multi-joint reactive torques based on center of mass (CoM) deviation. We evaluated the generalizability of our delayed CoM feedback controller to predict multi-joint torque responses to perturbations of varying magnitudes, directions, and across movement contexts. Methods: In nine healthy young adults, we tested the ability of a delayed CoM feedback scheme to predict multi-joint torque responses to 1) ramp-and-hold support surface perturbations at three magnitudes in 8 directions, 2) a continuous sinusoidal movement, mimicking cyclical tasks like walking, and 3) a sinusoidal motion with random perturbations superimposed to mimic perturbations during cyclic tasks. We trained the model on single ramp-and-hold conditions and evaluated its ability to generalize to all others. Results: The delayed CoM feedback controller trained on a single ramp-and-hold condition generalized to all ramp-and-hold perturbations for all joints, predicting the joint torques for perturbations of varying directions and magnitudes with high fidelity (average R Conclusion: Our findings show that a physiologically-inspired CoM feedback controller can robustly predict balance-correcting torques appropriate for driving a hip or knee wearable robotic device during standing and movement, and an ankle device during standing only. The goodness-of-fit to joint torque is comparable to top machine learning algorithms, yet requires orders of magnitude less training data, enabling rapid implementation to reduce fall risk.