Why animals can outrun robots.

Animals are much better at running than robots. The difference in performance arises in the important dimensions of agility, range, and robustness. To understand the underlying causes for this performance gap, we compare natural and artificial technologies in the five subsystems critical for running: power, frame, actuation, sensing, and control. With few exceptions, engineering technologies meet or exceed the performance of their biological counterparts. We conclude that biology's advantage over engineering arises from better integration of subsystems, and we identify four fundamental obstacles that roboticists must overcome. Toward this goal, we highlight promising research directions that have outsized potential to help future running robots achieve animal-level performance.

Instantaneous kinematic phase reflects neuromechanical response to lateral perturbations of running cockroaches.

Instantaneous kinematic phase calculation allows the development of reduced-order oscillator models useful in generating hypotheses of neuromechanical control. When perturbed, changes in instantaneous kinematic phase and frequency of rhythmic movements can provide details of movement and evidence for neural feedback to a system-level neural oscillator with a time resolution not possible with traditional approaches. We elicited an escape response in cockroaches (Blaberus discoidalis) that ran onto a movable cart accelerated laterally with respect to the animals' motion causing a perturbation. The specific impulse imposed on animals (0.50 [Formula: see text] 0.04 m s[Formula: see text]; mean, SD) was nearly twice their forward speed (0.25 [Formula: see text] 0.06 m s[Formula: see text]. Instantaneous residual phase computed from kinematic phase remained constant for 110 ms after the onset of perturbation, but then decreased representing a decrease in stride frequency. Results from direct muscle action potential recordings supported kinematic phase results in showing that recovery begins with self-stabilizing mechanical feedback followed by neural feedback to an abstracted neural oscillator or central pattern generator. Trials fell into two classes of forward velocity changes, while exhibiting statistically indistinguishable frequency changes. Animals pulled away from the side with front and hind legs of the tripod in stance recovered heading within 300 ms, whereas animals that only had a middle leg of the tripod resisting the pull did not recover within this period. Animals with eight or more legs might be more robust to lateral perturbations than hexapods.

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking.

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This article studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a nonparametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e., applied by the experimenter) and endogenously (i.e., generated by sensorimotor noise) explains observed performance improvements.

Effects of virtual reality environments on overground walking in people with Parkinson disease and freezing of gait.

BACKGROUND: Freezing of gait (FoG) is a common target of rehabilitative interventions for people with Parkinson disease (PD). Virtual reality (VR) holds potential for advancing research and clinical management of FoG through flexible creation of FoG-provoking environments that are not easily or safely replicated in the clinic. OBJECTIVE: The aim of this study was to investigate whether VR environments that replicate FoG-provoking situations would exacerbate gait impairments associated with FoG compared to unobstructed VR and physical laboratory environments. METHODS: Gait characteristics (pace, rhythm, variability, asymmetry, and postural control domains) and festination were measured using motion capture while people with PD walked in VR environments based on FoG-provoking situations (doorway, hallway, and crowd environments) compared to unobstructed VR and physical laboratory environments. The effect of VR environments was assessed using one-way repeated measures ANOVAs with planned contrasts. RESULTS: Ten participants (mean age 74.1 years, 3 females, Hoehn and Yahr stage 2-3) with PD who self-reported FoG participated. Gait speed and step length were reduced in all VR environments compared to the physical laboratory. Step width was wider, step length was more variable, and festination was more common for some of the VR environments compared to the physical laboratory environment. Compared to the unobstructed virtual laboratory environment, step length was more variable in VR crowd and doorway environments. CONCLUSIONS: The exacerbation of gait impairments that are characteristic precursors of FoG in FoG-provoking VR environments supports the potential utility of VR technology in the assessment and treatment of gait impairments in PD.Implications for rehabilitationFreezing increases fall risk and reduces quality of life in Parkinson disease (PD).Virtual reality (VR) can simulate visuospatial environments that provoke freezing.Immersive VR doorway, hallway, and crowd environments were developed.Gait speed slowed when people with PD walked overground in all VR environments.Step variability and festination increased in freeze-provoking environments.

Virtual reality doorway and hallway environments alter gait kinematics in people with Parkinson disease and freezing.

BACKGROUND: Many people with Parkinson disease (PD) experience freezing of gait (FoG), a transient gait disturbance associated with increased fall risk and reduced quality of life. Head-mounted virtual reality (VR) systems allow overground walking and can create immersive simulations of physical environments that induce FoG. RESEARCH QUESTION: For people with PD who experience FoG (PD+FoG), are kinematic gait changes observed in VR simulations of FoG-provoking environments? METHODS: In a cross-sectional experiment, people with PD+FoG walked at their self-selected speed in a physical laboratory and virtual laboratory, doorway, and hallway environments. Motion analysis assessed whole-body kinematics, including lower extremity joint excursions, swing phase toe clearance, trunk flexion, arm swing, sagittal plane inclination angle, and spatiotemporal characteristics. One-way repeated measures analysis of variance was conducted to examine the effects of environment on gait variables, with planned contrasts between laboratory environments and the virtual doorway and hallway. RESULTS: Twelve participants with PD+FoG (mean age [standard deviation]=72.8 [6.5] years, disease duration=8.8 [8.9] years, 3 females) completed the protocol. The environment had significant and widespread effects on kinematic and spatiotemporal variables. Compared to the physical laboratory, reduced joint excursions were observed in the ankle, knee, and hip when walking in the virtual doorway and in the knee and hip when walking in the virtual hallway. In both the virtual doorway and hallway compared to the physical laboratory, peak swing phase toe clearance, arm swing, and inclination angle were reduced, and walking was slower, with shorter, wider steps. SIGNIFICANCE: Virtual doorway and hallway environments induced kinematic changes commonly associated with FoG episodes, and these kinematic changes are consistent with forward falls that are common during FoG episodes. Combined with the flexibility of emerging VR technology, this research supports the potential of VR applications designed to improve the understanding, assessment, and treatment of FoG.

Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance.

Manual device interaction requires precise coordination which may be difficult for users with motor impairments. Muscle interfaces provide alternative interaction methods that may enhance performance, but have not yet been evaluated for simple (eg. mouse tracking) and complex (eg. driving) continuous tasks. Control theory enables us to probe continuous task performance by separating user input into intent and error correction to quantify how motor impairments impact device interaction. We compared the effectiveness of a manual versus a muscle interface for eleven users without and three users with motor impairments performing continuous tasks. Both user groups preferred and performed better with the muscle versus the manual interface for the complex continuous task. These results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.

Predicting walking response to ankle exoskeletons using data-driven models.

Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.