scholarly journals Learning to walk with a wearable robot in 880 simple steps: a pilot study on motor adaptation

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Florian L. Haufe ◽  
Alessia M. Kober ◽  
Peter Wolf ◽  
Robert Riener ◽  
Michele Xiloyannis
Keyword(s):  
Energy Expenditure ◽  
Internal Model ◽  
Feedforward Control ◽  
Statistical Parametric Mapping ◽  
Motor Adaptation ◽  
Optimization Techniques ◽  
Robotic Assistance ◽  
Wearable Robot ◽  
Mixed Effect ◽  
Human In The Loop

Abstract Background Wearable robots have been shown to improve the efficiency of walking in diverse scenarios. However, it is unclear how much practice is needed to fully adapt to robotic assistance, and which neuromotor processes underly this adaptation. Familiarization strategies for novice users, robotic optimization techniques (e.g. human-in-the-loop), and meaningful comparative assessments depend on this understanding. Methods To better understand the process of motor adaptation to robotic assistance, we analyzed the energy expenditure, gait kinematics, stride times, and muscle activities of eight naïve unimpaired participants across three 20-min sessions of robot-assisted walking. Experimental outcomes were analyzed with linear mixed effect models and statistical parametric mapping techniques. Results Most of the participants’ kinematic and muscular adaptation occurred within the first minute of assisted walking. After ten minutes, or 880 steps, the energetic benefits of assistance were realized (an average of 5.1% (SD 2.4%) reduction in energy expenditure compared to unassisted walking). Motor adaptation was likely driven by the formation of an internal model for feedforward motor control as evidenced by the reduction of burst-like muscle activity at the cyclic end of robotic assistance and an increase in arm-swing asymmetry previously associated with increased cognitive load. Conclusion Humans appear to adapt to walking assistance from a wearable robot over 880 steps by forming an internal model for feedforward control. The observed adaptation to the wearable robot is well-described by existing three-stage models that start from a cognitive stage, continue with an associative stage, and end in autonomous task execution. Trial registration Not applicable.

scholarly journals Internal model recalibration does not deteriorate with age while motor adaptation does

2019 ◽  
Vol 80 ◽  
pp. 138-153 ◽  
Author(s):  
Koenraad Vandevoorde ◽  
Jean-Jacques Orban de Xivry
Keyword(s):  
Internal Model ◽  
Motor Adaptation

Are fast/slow process in motor adaptation and forward/inverse internal model two sides of the same coin?

2013 ◽  
Vol 81 (4) ◽  
pp. 592-600 ◽  
Author(s):  
Fatemeh Yavari ◽  
Farzad Towhidkhah ◽  
Mohammad Ali Ahmadi-Pajouh
Keyword(s):  
Internal Model ◽  
Motor Adaptation ◽  
Slow Process ◽  
Two Sides

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

2020 ◽  
Author(s):  
Momona Yamagami ◽  
Lauren N. Peterson ◽  
Darrin Howell ◽  
Eatai Roth ◽  
Samuel A. Burden
Keyword(s):  
Control Systems ◽  
Trajectory Tracking ◽  
Disturbance Rejection ◽  
Feedforward Control ◽  
Hand Preference ◽  
Parametric Modeling ◽  
Loop Control ◽  
Human In The Loop ◽  
Control Dynamic ◽  
And Performance

AbstractIn 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 paper studies the effect of handedness on learned controllers and performance during a continuous 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 non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects’ controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise).

scholarly journals Internal model recalibration does not deteriorate with age while motor adaptation does

2018 ◽  
Author(s):  
Koenraad Vandevoorde ◽  
Jean-Jacques Orban de Xivry
Keyword(s):  
Older People ◽  
Older Women ◽  
Internal Model ◽  
Motor Adaptation ◽  
Cerebellar Degeneration ◽  
Motor Memory ◽  
Cognitive Component ◽  
Age Related ◽  
Wide Range ◽  
Interesting Possibility

AbstractA wide range of motor function declines with aging. Motor adaptation, which occurs when participants learn to reach accurately to a target despite a perturbation, does not deviate from this rule. There are currently three major hypotheses that have been put forward to explain this age-related decline in adaptation: deterioration of internal model recalibration due to age-related cerebellar degeneration, impairment of the cognitive component of motor adaptation, and deficit in the retention of the learned movement. In the present study, we systematically investigated these three hypotheses in a large sample of older women and men. We demonstrate that age-related deficits in motor adaptation are not due to impaired internal model recalibration or impaired retention of motor memory. Rather, we found that the cognitive component was reduced in older people. Therefore, our study suggests the interesting possibility that cerebellar-based mechanisms do not deteriorate with age despite cerebellar degeneration. In contrast, internal model recalibration appears to compensate for deficits in the cognitive component of this type of learning.

scholarly journals Does somatosensory acuity influence the extent of internal model recalibration in young and older adults?

2020 ◽  
Author(s):  
Koenraad Vandevoorde ◽  
Jean-Jacques Orban de Xivry
Keyword(s):  
Older Adults ◽  
Sensory Integration ◽  
Prediction Error ◽  
Internal Model ◽  
Sensory Feedback ◽  
Motor Adaptation ◽  
Age Related ◽  
Somatosensory Information ◽  
Implicit Motor ◽  
Sensory Prediction

AbstractThe ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, automatic adjustment of movement, or internal model recalibration, remains intact and might even be increased with aging. Since somatosensory information appears to be required for internal model recalibration, it might well be that an age-related decline in somatosensory acuity is linked to the increase of internal model recalibration. One possible explanation for an increased internal model recalibration is that age-related somatosensory deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced somatosensory acuity results in an increased reliance on predicted sensory feedback. Both these explanations led to our preregistered hypothesis: we expect a relation between the decline of somatosensation and the increased internal model recalibration with aging. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and somatosensory signals during motor adaptation.New & NoteworthyIs somatosensory acuity linked to implicit motor adaptation? The latter is larger in old compared to younger people? In light of reliability-based sensory integration, we hypothesized that this larger implicit adaptation was linked to an age-related lower reliability of somatosensation. Over two experiments and 130 participants, we failed to find any evidence for this. We discuss alternative explanations for the increase in implicit adaptation with age and the validity of our somatosensory assessment.

scholarly journals Feedforward and feedback control share an internal model of the arm’s dynamics

2018 ◽  
Author(s):  
Rodrigo S. Maeda ◽  
Tyler Cluff ◽  
Paul L. Gribble ◽  
J. Andrew Pruszynski
Keyword(s):  
Nervous System ◽  
Feedback Control ◽  
Shoulder Joint ◽  
Internal Model ◽  
Feedforward Control ◽  
Substantial Reduction ◽  
Baseline Phase ◽  
Intersegmental Dynamics ◽  
Adaptation Phase ◽  
Feedback Responses

AbstractHumans have a remarkable capacity to learn novel movement patterns in a wide variety of contexts. Recent work has shown that, when countering external forces, the nervous system adjusts not only voluntary (ie. feedforward) control but also reflex (ie. feedback) responses. Here we show that directly altering the physical properties of the arm (i.e. intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning also transfers to feedback responses even though they were never directly trained. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements with the shoulder joint either free to rotate or locked. Locking the shoulder joint cancels the interaction forces that arise at the shoulder during forearm rotation and thus removes the need to activate shoulder muscles to prevent shoulder joint rotation. We first asked whether the nervous system learns this altered mapping of intersegmental dynamics. In the baseline phase, we found robust activation of shoulder flexor muscles for pure elbow flexion trials prior to movement onset – as required to counter the intersegmental dynamics. After locking the shoulder joint in the adaptation phase, we found a substantial reduction in shoulder muscle activity over many trials. After unlocking the shoulder joint in the post-adaptation phase, we observed after-effects, as participants made systematic hand path errors. In our second experiment, we investigated whether such learning transfers to feedback control. Mechanical perturbations applied to the limb in the baseline phase revealed that feedback responses, like feedforward control, also appropriately countered intersegmental dynamics. In the adaptation phase, we found a substantial reduction in shoulder feedback responses – as appropriate for the altered intersegmental dynamics. We also found that this decay in shoulder feedback responses correlated across subjects with the amount of decay during feedforward control. Our work adds to the growing evidence that feedforward and feedback control share an internal model of the arm’s dynamics.

Sign in / Sign up

Export Citation Format

Share Document