Walking on Real-world Terrain with an Ankle Exoskeleton in Cerebral Palsy

Adaptive hip exoskeleton control using heart rate feedback reduces oxygen cost during ecological locomotion

Despite their potential, exoskeletons have not reached widespread adoption in daily life, partly due to the challenge of seamlessly adapting assistance across various tasks and environments. Task-specific designs, reliance on complex sensing and extensive data-driven training often limit the practicality of the existing control strategies. To address this challenge, we introduce an adaptive control strategy for hip exoskeletons, emphasizing minimal sensing and ease of implementation. Using only insole pressure and heart rate (HR) sensing, the controller modulates assistance across various locomotor tasks. We evaluated this strategy with twelve able-bodied participants in a real-world scenario including level walking, stairs, and inclines. The controller successfully adapted assistance timing and amplitude to different activities. This resulted in effort intensity reductions (measured by oxygen uptake) of up to 12.6% compared to walking with no exoskeleton, and up to 25.5% compared to walking with the exoskeleton in zero-torque mode. Cardiodynamic response of HR, although delayed, proved sufficient for adaptation in tasks lasting longer than around 45 s, and delay-induced limitations primarily affected brief bouts of abrupt change in intensity. However, we found discernible patterns in HR shortly after the onset of such changes that can be exploited to improve responsiveness. Our findings underscore the potential of HR as a promising measure of user effort intensity, encouraging future research to explore its integration into advanced adaptive algorithms.

Exoskeleton gait training on real-world terrain improves spatiotemporal performance in cerebral palsy

Introduction

Walking is essential for daily life but poses a significant challenge for many individuals with neurological conditions like cerebral palsy (CP), which is the leading cause of childhood walking disability. Although lower-limb exoskeletons show promise in improving walking ability in laboratory and controlled overground settings, it remains unknown whether these benefits translate to real-world environments, where they could have the greatest impact.

Methods

This feasibility study evaluated whether an untethered ankle exoskeleton with an adaptable controller can improve spatiotemporal outcomes in eight individuals with CP after low-frequency exoskeleton-assisted gait training on real-world terrain.

Results

Comparing post- and pre-assessment, assisted walking speed increased by 11% and cadence by 7% (p = 0.003; p = 0.006), while unassisted walking speed increased by 8% and cadence by 5% (p = 0.009; p = 0.012). In the post-assessment, assisted walking speed increased by 9% and stride length by 8% relative to unassisted walking (p < 0.001; p < 0.001). Improvements in walking speed were more strongly associated with longer strides than higher cadence (R 2 = 0.92; R 2 = 0.68). Muscle activity outcomes, including co-contraction of the soleus and tibialis anterior, did not significantly change after training.

Discussion

These findings highlight the spatiotemporal benefits of an adaptive ankle exoskeleton for individuals with CP in real-world settings after short-term training. This work paves the way for future randomized controlled trials (RCTs) to evaluate the isolated effects of adaptive ankle exoskeletons on gait performance and neuromuscular outcomes in individuals with CP in real-world environments.

Task-agnostic exoskeleton control via biological joint moment estimation

Lower-limb exoskeletons have the potential to transform the way we move1-14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-based method constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability.

Evaluation of the Working Mechanism of a Newly Developed Powered Ankle-Foot Orthosis

Ankle-foot orthoses (AFOs) are commonly prescribed to children with cerebral palsy (CP). The conventional AFO successfully controls the first and second ankle rocker, but it fails to correct the third ankle rocker, which negatively effects push-off power. The current study evaluated a new powered AFO (PAFO) design, developed to address the shortcomings of the conventional AFO. Eight children with spastic CP (12.4 ± 3.4 years; GMFCS I-III; 4/4-♂/♀; 3/5-bi/unilateral) were included. Sagittal kinematic and kinetic data were collected from 20 steps during barefoot walking, with conventional AFOs and PAFOs. In the PAFO-condition, an actuation unit was attached to a hinged AFO and through push-pull cables to a backpack that was carried by the child and provided patient-specific assistance-as-needed. SnPM-analysis indicated gait cycle sections that differed significantly between conditions. For the total group, differences between the three conditions were found in ankle kinematics (49.6-66.1%, p = 0.006; 88.0-100%, p = 0.011) and angular velocity (0.0-6.0%, p = 0.001; 45.1-51.1%, p = 0.006; 62.2-73.0%, p = 0.001; 81.2-93.0%, p = 0.001). Individual SnPM-analysis revealed a greater number of significant gait cycle sections for kinematics and kinetics of the ankle, knee, and hip. These individual results were heterogeneous and specific per gait pattern. In conclusion, the new PAFO improved the ankle range-of-motion, angular velocity, and power during push-off in comparison to the conventional AFO.

Feasibility of Using Autonomous Ankle Exoskeletons to Augment Community Walking in Cerebral Palsy

Objective: This pilot study investigated the feasibility and efficacy of using autonomous ankle exoskeletons in community settings among individuals with cerebral palsy (CP). Five participants completed two structured community walking protocols: a week-long ankle exoskeleton acclimation and training intervention, and a dose-matched Sham intervention of unassisted walking. Results: Results demonstrated significant improvements in acclimatized walking performance with the ankle exoskeleton, including increased speed and stride length. Participants also reported increased enjoyment and perceived benefits of using the exoskeleton. While ankle exoskeleton training did not lead to significant improvements in unassisted walking, this study demonstrates the feasibility of using ankle exoskeletons in the real world by people with CP. Conclusions: This study highlights the potential of wearable exoskeletons to augment community walking performance in CP, laying a foundation for further exploration in real-world environments.

Estimating human joint moments unifies exoskeleton control, reducing user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.