Robotic Ankle Exoskeleton Research Articles

Development and Validation of Robotic Ankle Exoskeleton With Parallel Nonlinear Elastic Actuator

Abstract This paper presents the development of a robotic ankle exoskeleton for human walking assistance. First, the biomechanical properties of a human ankle joint during walking are presented. Next, design of the robotic ankle exoskeleton is introduced. The exoskeleton is actuated by a novel parallel nonlinear elastic actuator. The cam-spring mechanism in the actuator can function as a parallel nonlinear spring with an adjustable stiffness, and the design of the cam profile curve is described. Additionally, an adaptive controller is proposed for the exoskeleton to generate a desired assistive torque according to the wearer's total weight. Finally, experiments are conducted to validate the effectiveness of the developed robotic ankle exoskeleton. The experimental results demonstrate that during a gait cycle, reductions of 42.7% and 40.1% of the peak and average currents of the driving motor in the actuator are observed, respectively, with the designed cam-spring mechanism. A peak assistive torque of 23.9 Nm can be provided for the wearers by the exoskeleton during walking. With the assistance provided by the exoskeleton, the average and peak soleus activities of the wearers during a gait cycle are decreased by 25.42% and 31.94%, respectively.

Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control.

Lower limb robotic exoskeletons are often studied in the context of steady-state treadmill walking in laboratory environments. However, the end goal of these devices is often adoption into our everyday lives. To move outside of the laboratory, there is a need to study exoskeletons in real world, complex environments. One way to study the human-machine interaction is to look at how the exoskeleton affects the user's gait. In this study we assessed changes in gait spatiotemporal variability when using a robotic ankle exoskeleton under proportional myoelectric control both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would not lead to significant changes in variability inside on a treadmill or outside compared to not using the exoskeletons. In addition, we hypothesized that walking outside would lead to higher variability both with and without the exoskeletons compared to treadmill walking. In support of our hypothesis, we found significantly higher coefficients of variation of stride length, stance time, and swing time when walking outside both with and without the exoskeleton. We found a significantly higher variability when using the exoskeletons inside on the treadmill, but we did not see significantly higher variability when walking outside overground. The value of this study to the literature is that it emphasizes the importance of studying exoskeletons in the environment in which they are meant to be used. By looking at only indoor gait spatiotemporal measures, we may have assumed that the exoskeletons led to higher variability which may be unsafe for certain target populations. In the context of the literature, we show that variability due to robotic ankle exoskeletons under proportional myoelectric control does not elicit different changes in stride time variability than previously found in other daily living tasks (uneven terrain, load carriage, or cognitive tasks).

An Energetic Approach to Task-Invariant Ankle Exoskeleton Control.

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle exoskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.

Predicting Neuromuscular Engagement to Improve Gait Training with a Robotic Ankle Exoskeleton.

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton's onboard sensors, and demonstrated 85-87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.

Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors.

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy

ABSTRACT A current limitation in the development of robotic gait training interventions is understanding the factors that predict responses to treatment. The purpose of this study was to explore the application of an interpretable machine learning method, Bayesian Additive Regression Trees (BART), to identify factors influencing neuromuscular responses to a resistive ankle exoskeleton in individuals with cerebral palsy (CP). Eight individuals with CP (GMFCS levels I – III, ages 12–18 years) walked with a resistive ankle exoskeleton over seven visits while we measured soleus activation. A BART model was developed using a predictor set of kinematic, device, study, and participant metrics that were hypothesized to influence soleus activation. The model (R 2 = 0.94) found that kinematics had the largest influence on soleus activation, but the magnitude of exoskeleton resistance, amount of gait training practice with the device, and participant-level parameters also had substantial effects. To optimize neuromuscular engagement during exoskeleton training in individuals with CP, our analysis highlights the importance of monitoring the user’s kinematic response, in particular, peak stance phase hip flexion and ankle dorsiflexion. We demonstrate the utility of machine learning techniques for enhancing our understanding of robotic gait training outcomes, seeking to improve the efficacy of future interventions.

Preliminary Validation of Proportional Myoelectric Control of A Commercially Available Robotic Ankle Exoskeleton.

Proportional myoelectric control of robotic lower limb exoskeletons can increase the variability and adaptability of biomechanical behaviors for assisting human movement compared to traditional state-based control. Previous exoskeletons using proportional myoelectric control have relied on pneumatic actuators and been limited to laboratory use. We applied proportional myoelectric control to a robotic ankle exoskeleton using a brushless DC motor (Dephy) and enabled it to work in community settings. Benchtop testing verified electromechanical responses similar to biological values (electromechanical delay of 22 ms and time to peak activation of 123 ms). Four healthy participants trained for thirty minutes each using bilateral ankle exoskeletons. From minute one of powered walking to minute 30 of powered walking, peak soleus EMG reduced by 17.9% as they learned to walk with exoskeleton assistance. Our future work will extend the powered walking period, measure metabolic cost, and measure gait variability between participants using proportional myoelectric control on fully portable, electromechanical ankle exoskeletons.

Virtual Neuromuscular Control for Robotic Ankle Exoskeleton Standing Balance

The exoskeleton is often regarded as a tool for rehabilitation and assistance of human movement. The control schemes were conventionally implemented by developing accurate physical and kinematic models, which often lack robustness to external variational disturbing forces. This paper presents a virtual neuromuscular control for robotic ankle exoskeleton standing balance. The robustness of the proposed method was improved by applying a specific virtual neuromuscular model to estimate the desired ankle torques for ankle exoskeleton standing balance control. In specialty, the proposed control method has two key components, including musculoskeletal mechanics and neural control. A simple version of the ankle exoskeleton was designed, and three sets of comparative experiments were carried out. The experimentation results demonstrated that the proposed virtual neuromuscular control could effectively reduce the wearer’s lower limb muscle activation, and improve the robustness of the different external disturbances.

Design and Electromechanical Performance Evaluation of a Powered Parallel-Elastic Ankle Exoskeleton

The widespread adoption of powered lower-limb exoskeletons for augmenting mobility requires energy-efficient actuation to provide meaningful assistance over relevant walking durations. We designed, modeled, and validated an ankle exoskeleton design with a parallel elastic element in the form of a carbon fiber leaf spring that stored and returned energy in parallel to a cable-drive ankle joint during stance phase. We assessed the impact of the parallel elastic element on the performance of the untethered robotic ankle exoskeleton at walking speeds of 0.75–1.25 m/s using a previously validated-controller, and a controller designed specifically to maximize the spring's benefit. The spring selected for our adult cohort had a stiffness of 97.2 Nm/rad, engaged best at 0-degrees ankle dorsiflexion, and produced 10–15 Nm peak assistive torque at all walking speeds. When tracking the previously validated exoskeleton controller, peak motor current was reduced by 14–20% and integrated current was reduced by 16–19% for parallel-elastic design vs. without spring engagement; this translated to 15–26% more assisted steps for the same battery capacity. When utilizing the controller designed to take advantage of the parallel spring torque, the number of assisted steps for the same battery capacity increased 46–76% compared to the no spring configuration depending on walking speed. Seeking to facilitate real-world adoption, this powered parallel elastic ankle exoskeleton design holds potential to significantly extend powered walking duration, and improve battery and motor life of operation by reducing peak and mean motor current requirements.

Actuation Timing Perception of a Powered Ankle Exoskeleton and Its Associated Ankle Angle Changes During Walking.

Robotic ankle exoskeletons have the potential to extend human ability, and actuation timing serves as one of the critical parameters in its controller design. While many experiments have investigated the optimal actuation timing values to achieve different objective functions (e.g. minimizing metabolic cost), studies on users' perception of control parameters are gaining interest as it gives information on people's comfort, coordination, and trust in using devices, as well as providing foundations on how the sensorimotor system detects the exoskeleton behavior changes. The purpose of this study was to evaluate people's sensitivity to changes in exoskeleton actuation timing and its associated exoskeleton ankle angle changes during walking. Participants (n =15) with little or no prior experience with ankle exoskeletons were recruited and performed a psychophysical experiment to characterize their just-noticeable difference (JND) thresholds for actuation timing. Participants wore a bilateral active ankle exoskeleton and compared pairs of torque profiles with different actuation timings and low peak torque (0.225 Nm/kg) while walking on the treadmill. The mean timing JND across participants was 2.8±0.6% stride period. Individuals exhibited different sensitivity towards actuation timing, and their associated exoskeleton ankle angle changes also varied. The variance in ankle angle changes might be explained by their differences in ankle stiffness and different ankle torques provided during walking. The results provide insights into how people perceive the changes in exoskeleton control parameters and show individual differences in exoskeleton usage. The actuation timing JND found in this study can also help determine the necessary controller precision.

Usability and performance validation of an ultra-lightweight and versatile untethered robotic ankle exoskeleton

BackgroundAnkle exoskeletons can improve walking mechanics and energetics, but few untethered devices have demonstrated improved performance and usability across a wide range of users and terrains. Our goal was to design and validate a lightweight untethered ankle exoskeleton that was effective across moderate-to-high intensity ambulation in children through adults with and without walking impairment.MethodsFollowing benchtop validation of custom hardware, we assessed the group-level improvements in walking economy while wearing the device in a diverse unimpaired cohort (n = 6, body mass = 42–92 kg). We also conducted a maximal exertion experiment on a stair stepping machine in a small cohort of individuals with cerebral palsy (CP, n = 5, age = 11–33 years, GMFCS I-III, body mass = 40–71 kg). Device usability metrics (device don and setup times and System Usability Score) were assessed in both cohorts.ResultsThere was a 9.9 ± 2.6% (p = 0.012, range = 0–18%) reduction in metabolic power during exoskeleton-assisted inclined walking compared to no device in the unimpaired cohort. The cohort with CP was able to ascend 38.4 ± 23.6% (p = 0.013, range = 3–132%) more floors compared to no device without increasing metabolic power (p = 0.49) or perceived exertion (p = 0.50). Users with CP had mean device don and setup times of 3.5 ± 0.7 min and 28 ± 6 s, respectively. Unimpaired users had a mean don time of 1.5 ± 0.2 min and setup time of 14 ± 1 s. The average exoskeleton score on the System Usability Scale was 81.8 ± 8.4 (“excellent”).ConclusionsOur battery-powered ankle exoskeleton was easy to use for our participants, with initial evidence supporting effectiveness across different terrains for unimpaired adults, and children and adults with CP.Trial registration Prospectively registered at ClinicalTrials.gov (NCT04119063) on October 8, 2019.

Neuromechanics and Energetics of Walking With an Ankle Exoskeleton Using Neuromuscular-Model Based Control: A Parameter Study.

Powered ankle exoskeletons that apply assistive torques with optimized timing and magnitude can reduce metabolic cost by ∼10% compared to normal walking. However, finding individualized optimal control parameters is time consuming and must be done independently for different walking modes (e.g., speeds, slopes). Thus, there is a need for exoskeleton controllers that are capable of continuously adapting torque assistance in concert with changing locomotor demands. One option is to use a biologically inspired, model-based control scheme that can capture the adaptive behavior of the human plantarflexors during natural gait. Here, based on previously demonstrated success in a powered ankle-foot prosthesis, we developed an ankle exoskeleton controller that uses a neuromuscular model (NMM) comprised of a Hill type musculotendon driven by a simple positive force feedback reflex loop. To examine the effects of NMM reflex parameter settings on (i) ankle exoskeleton mechanical performance and (ii) users’ physiological response, we recruited nine healthy, young adults to walk on a treadmill at a fixed speed of 1.25 m/s while donning bilateral tethered robotic ankle exoskeletons. To quantify exoskeleton mechanics, we measured exoskeleton torque and power output across a range of NMM controller Gain (0.8–2.0) and Delay (10–40 ms) settings, as well as a High Gain/High Delay (2.0/40 ms) combination. To quantify users’ physiological response, we compared joint kinematics and kinetics, ankle muscle electromyography and metabolic rate between powered and unpowered/zero-torque conditions. Increasing NMM controller reflex Gain caused increases in average ankle exoskeleton torque and net power output, while increasing NMM controller reflex Delay caused a decrease in net ankle exoskeleton power output. Despite systematic reduction in users’ average biological ankle moment with exoskeleton mechanical assistance, we found no NMM controller Gain or Delay settings that yielded changes in metabolic rate. Post hoc analyses revealed weak association at best between exoskeleton and biological mechanics and changes in users’ metabolic rate. Instead, changes in users’ summed ankle joint muscle activity with powered assistance correlated with changes in their metabolic energy use, highlighting the potential to utilize muscle electromyography as a target for on-line optimization in next generation adaptive exoskeleton controllers.

Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game.

Background: Robotic devices have been used to rehabilitate walking function after stroke. Although results suggest that post-stroke patients benefit from this non-conventional therapy, there is no agreement on the optimal robot-assisted approaches to promote neurorecovery. Here we present a new robotic therapy protocol using a grounded exoskeleton perturbing the ankle joint based on tacit learning control.Method: Ten healthy individuals and a post-stroke patient participated in the study and were enrolled in a pilot intervention protocol that involved performance of ankle movements following different trajectories via video game visual feedback. The system autonomously modulated task difficulty according to the performance to increase the challenge. We hypothesized that motor learning throughout training sessions would lead to increased corticospinal excitability of dorsi-plantarflexor muscles. Transcranial Magnetic Stimulation was used to assess the effects on corticospinal excitability.Results: Improvements have been observed on task performance and motor outcomes in both healthy individuals and post-stroke patient case study. Tibialis Anterior corticospinal excitability increased significantly after the training; however no significant changes were observed on Soleus corticospinal excitability. Clinical scales showed functional improvements in the stroke patient.Discussion and Significance: Our findings both in neurophysiological and performance assessment suggest improved motor learning. Some limitations of the study include treatment duration and intensity, as well as the non-significant changes in corticospinal excitability obtained for Soleus. Nonetheless, results suggest that this robotic training framework is a potentially interesting approach that can be explored for gait rehabilitation in post-stroke patients.

Kinematics of underactuated robotics for product carbon footprint

Product low-carbon design has become one of the important means of sustainable manufacturing. An important design principle should be to minimize the weight of the robot while maximizing its motion efficiency. Therefore, how to develop and analyze low-carbon robots is the focus of this paper. The illustration used in the paper is a novel proposal for an underactuated robotic ankle exoskeleton for human workers. Conventional robots have enough actuators to perform independent movements. In contrast, an underactuated device emphasizes the use of natural dynamic control systems. Kinematics does not take into account the causal force of motion to study moving objects. This paper uses kinematic ankle joint as an example to analyze kinematics. The human worker is the causal force for an exoskeleton design. In order to make it easier for human to use, this paper proposes a joint exoskeleton based on underactuated spherical parallel mechanism. The underactuated mechanism has reduced a lot of unnecessary weight and added more degree of freedom compared with the traditional mechanism. Through the optimal design of the mechanism to reduce its carbon footprint through its life cycle. The carbon footprint analysis of the underactuated robotic ankle exoskeleton is then discussed in detail, which is given as an example to demonstrate this approach.

Artificial Human Balance Control by Calf Muscle Activation Modelling

The natural neuromuscular model has greatly inspired the development of control mechanisms in addressing the uncertainty challenges in robotic systems. Although the underpinning neural reaction of posture control remains unknown, recent studies suggest that muscle activation driven by the nervous system plays a key role in human postural responses to environmental disturbance. Given that the human calf is mainly formed by two muscles, this paper presents an integrated calf control model with the two comprising components representing the activations of the two calf muscles. The contributions of each component towards the artificial control of the calf are determined by their weights, which are carefully designed to simulate the natural biological calf. The proposed calf modelling has also been applied to robotic ankle exoskeleton control. The proposed work was validated and evaluated by both biological and engineering simulation approaches, and the experimental results revealed that the proposed model successfully performed over 92% of the muscle activation naturally made by human participants, and the actions led by the simulated ankle exoskeleton wearers were overall consistent with that by the natural biological response.

Personalised Control of Robotic Ankle Exoskeleton Through Experience-Based Adaptive Fuzzy Inference

Robotic exoskeletons have emerged as effective rehabilitation and ability-enhancement tools, by mimicking or supporting natural body movements. The control schemes of exoskeletons are conventionally developed based on fixed torque-ankle state relationship or various human models, which are often lack of flexibility and adaptability to accurately address personalized movement assistance needs. This paper presents an adaptive control strategy for personalized robotic ankle exoskeleton in an effort to address this limitation. The adaptation was implemented by applying the experience-based fuzzy rule interpolation approach with the support of a muscle-tendon complex model. In particular, this control system is initialized based on the most common requirements of a “standard human model,” which is then evolved during its performance by effectively using the feedback collected from the wearer to support different body shapes and assistance needs. The experimental results based on different human models with various support demands demonstrate the power of the proposed control system in improving the adaptability, and thus applicability, of robotic ankle exoskeletons.

Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control

BackgroundControllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not.MethodsThe two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user’s soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment.ResultsWe observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller.ConclusionsWe interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.

Varying negative work assistance at the ankle with a soft exosuit during loaded walking

BackgroundOnly very recently, studies have shown that it is possible to reduce the metabolic rate of unloaded and loaded walking using robotic ankle exoskeletons. Some studies obtained this result by means of high positive work assistance while others combined negative and positive work assistance. There is no consensus about the isolated contribution of negative work assistance. Therefore, the aim of the present study is to examine the effect of varying negative work assistance at the ankle joint while maintaining a fixed level of positive work assistance with a multi-articular soft exosuit.MethodsWe tested eight participants during walking at 1.5 ms−1 with a 23-kg backpack. Participants wore a version of the exosuit that assisted plantarflexion via Bowden cables tethered to an off-board actuation platform. In four active conditions we provided different rates of exosuit bilateral ankle negative work assistance ranging from 0.015 to 0.037 W kg−1 and a fixed rate of positive work assistance of 0.19 W kg−1.ResultsAll active conditions significantly reduced metabolic rate by 11 to 15% compared to a reference condition, where the participants wore the exosuit but no assistance was provided. We found no significant effect of negative work assistance. However, there was a trend (p = .08) toward greater reduction in metabolic rate with increasing negative work assistance, which could be explained by observed reductions in biological ankle and hip joint power and moment.ConclusionsThe non-significant trend of increasing negative work assistance with increasing reductions in metabolic rate motivates the value in further studies on the relative effects of negative and positive work assistance. There may be benefit in varying negative work over a greater range or in isolation from positive work assistance.

Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton.

BackgroundRobotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user’s muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user’s myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain.MethodsWe tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user’s peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms-1. We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics.ResultsUsing our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power.ConclusionsOur findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-015-0086-5) contains supplementary material, which is available to authorized users.

Short-term locomotor adaptation to a robotic ankle exoskeleton does not alter soleus Hoffmann reflex amplitude.

BackgroundTo improve design of robotic lower limb exoskeletons for gait rehabilitation, it is critical to identify neural mechanisms that govern locomotor adaptation to robotic assistance. Previously, we demonstrated soleus muscle recruitment decreased by ~35% when walking with a pneumatically-powered ankle exoskeleton providing plantar flexor torque under soleus proportional myoelectric control. Since a substantial portion of soleus activation during walking results from the stretch reflex, increased reflex inhibition is one potential mechanism for reducing soleus recruitment when walking with exoskeleton assistance. This is clinically relevant because many neurologically impaired populations have hyperactive stretch reflexes and training to reduce the reflexes could lead to substantial improvements in their motor ability. The purpose of this study was to quantify soleus Hoffmann (H-) reflex responses during powered versus unpowered walking.MethodsWe tested soleus H-reflex responses in neurologically intact subjects (n=8) that had trained walking with the soleus controlled robotic ankle exoskeleton. Soleus H-reflex was tested at the mid and late stance while subjects walked with the exoskeleton on the treadmill at 1.25 m/s, first without power (first unpowered), then with power (powered), and finally without power again (second unpowered). We also collected joint kinematics and electromyography.ResultsWhen the robotic plantar flexor torque was provided, subjects walked with lower soleus electromyographic (EMG) activation (27-48%) and had concomitant reductions in H-reflex amplitude (12-24%) compared to the first unpowered condition. The H-reflex amplitude in proportion to the background soleus EMG during powered walking was not significantly different from the two unpowered conditions.ConclusionThese findings suggest that the nervous system does not inhibit the soleus H-reflex in response to short-term adaption to exoskeleton assistance. Future studies should determine if the findings also apply to long-term adaption to the exoskeleton.