Evidence map›Paper›PMID 26536868›Full record

ArticleJournal of neuroengineering and rehabilitation2015

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

Jeffrey R Koller, Daniel A Jacobs, Daniel P Ferris, C David Remy

Open access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 1 synthesis or guideline pooled it, 171 citations in OpenAlex.

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  8. Optimal Energy Shaping and Force Amplification Framework for Task-Agnostic, Biomimetic Ankle Exoskeletons.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2026
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  18. An Energetic Approach to Task-Invariant Ankle Exoskeleton Control.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2023
    Article
  19. Review
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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Jeffrey R KollerDepartment of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, 48109, MI, USA. jrkoller@umich.edu.
Daniel A JacobsSchool of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, 48109, MI, USA. jacobsda@umich.edu.
Daniel P FerrisSchool of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, 48109, MI, USA. ferrisdp@umich.edu.
C David RemyDepartment of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, 48109, MI, USA. jacobsda@umich.edu.
University of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Exoskeleton DeviceAdultAnkleAnkle JointBiomechanical PhenomenaElectromyographyEnergy MetabolismFemaleGaitHumansMaleMuscle, SkeletalRoboticsWalking

Identifiers

PMID26536868
PMCPMC4634144
OpenAlexW2149646777

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.