Adaptive Negative Damping Control for User-Dependent Multi-Terrain Walking Assistance with a Hip Exoskeleton

TL;DR

This work introduces an adaptive virtual negative damping control for hip exoskeletons that injects energy while keeping wearers in control, modeled on a compass-gait framework. The assistive torque is defined as $\Gamma_{exo} = \beta \mathbf{R} \dot{\mathbf{q}}_{hip}$ with a bounded $\beta$, and the velocity signals are filtered to ensure smooth actuation. Real-time Bayesian Optimization tunes $\beta$ based on hip kinematics, enabling seamless adaptation across flat and multi-terrain walking without explicit terrain recognition. Experimental results with five subjects show a mean metabolic cost reduction of $7.2\%$ and preservation of kinematics, with low energy losses ($<$2\% negative power on treadmill) and robust performance in unstructured environments where torque strength adapts to terrain demands. Overall, the approach provides individualized, adaptable, and practical control for hip exoskeletons, advancing user-dependent, terrain-aware assistive strategies.

Abstract

Hip exoskeletons are known for their versatility in assisting users across varied scenarios. However, current assistive strategies often lack the flexibility to accommodate for individual walking patterns and adapt to diverse locomotion environments. In this work, we present a novel control strategy that adapts the mechanical impedance of the human-exoskeleton system. We design the hip assistive torques as an adaptive virtual negative damping, which is able to inject energy into the system while allowing the users to remain in control and contribute voluntarily to the movements. Experiments with five healthy subjects demonstrate that our controller reduces the metabolic cost of walking compared to free walking (average reduction of 7.2%), and it preserves the lower-limbs kinematics. Additionally, our method achieves minimal power losses from the exoskeleton across the entire gait cycle (less than 2% negative mechanical power out of the total power), ensuring synchronized action with the users' movements. Moreover, we use Bayesian Optimization to adapt the assistance strength and allow for seamless adaptation and transitions across multi-terrain environments. Our strategy achieves efficient power transmission under all conditions. Our approach demonstrates an individualized, adaptable, and straightforward controller for hip exoskeletons, advancing the development of viable, adaptive, and user-dependent control laws.

Figures (9)

• Figure 1: The user-dependent and multi-terrain walking assistance with the hip exoskeleton eWalk. Participants walked on treadmill and in varied unstructured environments.
• Figure 2: (A) Schematic representation of our control framework. The torque profile is defined by a control law consisting of a Virtual Adaptive Negative Damping. The multiplicative coefficient ($\beta$) for the assistance strength is optimized with a Bayesian Optimization (BO). (B) Schematic representation of the compass-gait model and its main variables, where $q_1$ represents the hip angle of the stance leg, and $q_2$ the hip angle of the swing leg.
• Figure 3: Representation of the walking path for the varied terrain locomotion session (top view). The terrain consists of stairs (up and down), flat bouts, and changes of direction.
• Figure 4: Hip exoskeleton torque profiles resulting from our control strategy, for each participant to the experiment, when walking on the treadmill.
• Figure 5: (A) Power profiles calculated as the product of the hip exoskeleton torque and the hip joint angular velocities, for each participant. (B) Percentage of negative power area with respect to the total power area.