Wearable robotic exoskeletons hold great promise for gait rehabilitation as portable, accessible tools. However, a better understanding of the potential for exoskeletons to elicit neural adaptation-a critical component of neurological gait rehabilitation-is needed. In this study, we investigated whether humans adapt to bilateral asymmetric stiffness perturbations applied by a hip exoskeleton, taking inspiration from the asymmetry augmentation strategies used in split-belt treadmill training. During walking, we applied torques about the hip joints to repel the thigh away from a neutral position on the left side and attract the thigh toward a neutral position on the right side. Six participants performed an adaptation walking trial on a treadmill while wearing the exoskeleton. The exoskeleton elicited time-varying changes and aftereffects in step length and propulsive/braking ground reaction forces, indicating behavioral signatures of neural adaptation. These responses resemble typical responses to split-belt treadmill training, suggesting that the proposed intervention with a robotic hip exoskeleton may be an effective approach to (re)training symmetric gait.

Wearable robotic exoskeletons show promise in their ability to provide gait assistance and rehabilitation in real-world contexts. However, a better understanding is needed of how exoskeletons contribute to neural adaptation in locomotion, a critical component of neurological gait rehabilitation. We tested whether unilateral perturbations elicit neural adaptation in healthy participants using a novel robotic hip exoskeleton, taking inspiration from asymmetry augmentation strategies used in split-belt treadmill training. We found that applying a virtual stiffness parallel to the hip joint on one side elicited changes in hip range of motion and step length, and that these changes were time varying, indicating an adaptation response. However, participants converged on asymmetric hip ranges of motion and step lengths both with and without applied stiffness from the exoskeleton. These results suggest that while adaptation appears to have occurred, it was not solely driven by the nervous system reducing gait asymmetry. Our findings indicate that applying mechanical impedance asymmetrically to the joints may be an effective gait training and rehabilitation approach, as well as a method to elicit a novel adaptation response to further study neuromotor control of locomotion.

Humans have an astonishing ability to extract hidden information from the movement of others. In previous work, subjects observed the motion of a simulated stick-figure, two-link planar arm and estimated its stiffness. Fundamentally, stiffness is the relation between force and displacement. Given that subjects were unable to physically interact with the simulated arm, they were forced to make their estimates solely based on observed kinematic information. Remarkably, subjects were able to correctly correlate their stiffness estimates with changes in the simulated stiffness, despite the lack of force information. We hypothesized that subjects were only able to do this because the controller used to produce the simulated arm's movement, composed of oscillatory motions driving mechanical impedances, resembled the controller humans use to produce their own movement. However, it is still unknown what motion features subjects used to estimate stiffness. Human motion exhibits systematic velocity-curvature patterns, and it has previously been shown that these patterns play an important role in perceiving and interpreting motion. Thus, we hypothesized that manipulating the velocity profile should affect subjects' ability to estimate stiffness. To test this, we changed the velocity profile of the simulated two-link planar arm while keeping the simulated joint paths the same. Even with manipulated velocity signals, subjects were still able to estimate changes in simulated joint stiffness. However, when subjects were shown the same simulated path with different velocity profiles, they perceived motions that followed a veridical velocity profile to be less stiff than that of a non-veridical profile. These results suggest that path information (displacement) predominates over temporal information (velocity) when humans use visual observation to estimate stiffness.

Prior experiments have shown that human gait synchronizes to periodic torque pulses applied about the hip and ankle joints by robotic exoskeletons. Importantly, entrainment occurred even when the pulse period differed slightly from the user’s preferred stride period, making it a viable approach to increase gait speed. As gait speed is an important outcome of gait therapy, gait entrainment to mechanical perturbations may serve as a promising new method of robot-aided therapy. Still, an understanding of the underlying neuromechanical processes that give rise to gait entrainment is needed to fully evaluate its therapeutic potential. To gain such insight, the goal of this paper was to evaluate whether an existing neuromechanical model of human locomotion exhibited entrainment behavior similar to that observed in the prior human experiments. Simulation results showed that the model entrained to pulses applied at both the ankle and hip joints. The convergence of relative phase between model gait and hip perturbations was similar to that observed with the human gait, but differed slightly for ankle perturbations. Thus, models that can more accurately describe neuromechanical interactions between human gait and robotic exoskeletons are still needed. Nevertheless, the simulation results support the notion that the limit-cycle behavior observed during locomotion does not require supra-spinal control or a self-sustaining oscillatory neural network, which has important implications for improving gait therapy.

Restoring healthy kinematics is a critical component of assisting and rehabilitating impaired locomotion. Here we tested whether spatiotemporal gait patterns can be modulated by applying mechanical impedance to hip joints. Using the Samsung GEMS-H exoskeleton, we emulated a virtual spring (positive and negative) between the user's legs. We found that applying positive stiffness with the exoskeleton decreased stride time and hip range of motion for healthy subjects during treadmill walking. Conversely, the application of negative stiffness increased stride time and hip range of motion. These effects did not vary over long nor short repeated exposures to applied stiffness. In addition, minimal transient behavior was observed in spatiotemporal measures of gait when the stiffness controller transitioned between on and off states. These results suggest that changes in gait behavior induced by applying hip stiffness were purely a mechanical effect. Together, our findings indicate that applying mechanical impedance using lower-limb assistive devices may be an effective, minimally-encumbering intervention to restore healthy gait patterns.

The locomotion and balance capabilities of bipedal robots have greatly improved in recent years. However, maintaining balance on difficult terrain still poses a significant challenge. In this paper, we examined how humans maintain mediolateral balance when standing on a narrow beam with bare feet and wearing rigid soles. Our results show that foot-beam interaction dynamics critically influence balancing behavior. Importantly, this suggests that differences in human balancing behavior across different support surfaces may not solely result from changes in their neural control strategy. They may also result from changes in foot-ground interaction. Thus, the altered foot-ground interaction dynamics must be considered to accurately capture changes in the human controller across different support surfaces. A simplified model of foot-beam interaction was added to a double inverted pendulum model for human balancing. This extended model could replicate the change in human behavior across different foot contact conditions (bare feet vs. rigid feet). A better understanding of how humans coordinate whole-body behavior across a range of conditions may inform the development of balance controllers for bipedal robots.

While rehabilitation of upper-limb motor function with human-interactive robots has been met with success, robot-aided locomotor rehabilitation has proven challenging. To inform more effective approaches to robotic gait therapy, it is important to understand neuro-mechanical dynamics and control of unimpaired locomotion. Our previous studies reported that human gait entrained to periodic mechanical perturbations at the ankle when the perturbation period was close to preferred walking cadence. Moreover, entrainment was accompanied by synchronizing the perturbations to a constant gait phase, the same for all subjects, where they provided mechanical assistance. To test the generality of entrainment-based assistance, the present study evaluated the behavior of live unimpaired subjects who walked overground while wearing a hip exoskeleton robot. Periodic torque pulses were applied to the subjects' hips, with a period different from, but close to, their preferred stride cadence. Results indicated that unimpaired subjects entrained their gait to periodic mechanical perturbations at the hip. Convergence of relative phase between gait and perturbations was observed, but clustered around two distinct gait phases, in contrast to the single converged phase observed in entrainment to periodic ankle torques. These entrainment studies quantify important aspects of the nonlinear neuro-mechanical dynamics underlying the control of walking, which will inform the development of effective approaches to robotic walking therapy.

Standing on a beam is a challenging motor skill that requires the regulation of upright balance and stability. In this paper, we analyzed the behavior of humans balancing on a narrow beam without footwear. The results revealed high anti-correlation between lumped upper- and lower-body angular momentum. Despite differences in gross measures of balance, interlimb coordination was consistent between the novice and expert subjects, suggesting that both performances could be described with the same balance controller. By simulating a double inverted pendulum model utilizing different balancing controllers described in the robotics literature, we identified that the whole behavior observed from humans standing on a beam was best replicated with controllers that predominantly utilized hip actuation.

Throwing is a uniquely human skill that requires a high degree of coordination to successfully hit a target. Timing of ball release appears crucial as previous studies report required timing accuracies as short as 1-2ms, which however appear physiologically challenging. This study mathematically and experimentally demonstrates that humans can overcome these seemingly stringent timing requirements by shaping their hand trajectories to create extended timing windows, where ball releases achieve target hits despite temporal imprecision. Subjects practiced four task variations in a virtual environment, each with a distinct geometry of the solution space and different demands for timing. Model-based analyses of arm trajectories revealed that subjects first decreased timing error, followed by lengthening timing windows in their hand trajectories. This pattern was invariant across solution spaces, except for a control case. Hence, the exquisite skill that humans evolved for throwing is achieved by developing strategies that are less sensitive to temporal variability arising from neuromotor noise. This analysis also provides an explanation why coaches emphasize the "follow-through" in many ball sports.

For robotic systems to interact with or learn from the actions of surrounding humans, it is important that they can accurately interpret the intention driving human motor actions. Making such interpretations, however, requires the ability to perceive the relevant feature(s) from the observed human behavior. With visual sensing alone, robots are typically limited to perceiving only the human's overt motion in the form of joint angles and positions. Ideally, robots designed to interface with humans would also be able to infer information as to how the human is controlling itself from that overt motion. In this study, we investigated if and how humans might be able to visually sense changes in limb mechanical impedance of others. Results indicated that humans can visually perceive changes in joint stiffness from the motion of a two-link planar arm, suggesting that humans can extract information regarding how humans control limb impedance from kinematic information. These findings have important implications for applications where robots must interpret the motor actions of humans, such as during robot imitation learning and human-robot physical interaction.