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

## TL;DR

This paper introduces a new control framework for wearable robots that uses center of mass feedback to predict joint torques, showing it can generalize across different types of balance perturbations.

## Contribution

A physiologically-inspired CoM feedback controller that generalizes multi-joint torque predictions with minimal training data.

## Key findings

- The delayed CoM feedback controller generalized well to all ramp-and-hold perturbations with high accuracy.
- The controller generalized from standing to cyclic movement for hip and knee flexion but not for ankle plantarflexion.
- The model's performance was comparable to top machine learning algorithms but required much less training data.

## Abstract

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.

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.

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 R2 > 0.84 and RMSE < 0.08 Nm/kg). However, generalization from standing to cyclic movement only occurred for hip and knee flexion. The CoM feedback parameters from ramp-and-hold perturbations generalized to the continuous sinusoidal movement (cyclic movement) and the sinusoidal movement with superimposed perturbations (unexpected perturbations) for hip flexion and knee flexion (average R2>0.70 and RMSE < 0.13 Nm/kg), but not for ankle plantarflexion and hip adduction (R2>0.20 and RMSE < 0.22 Nm/kg).

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.