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J Neuroeng Rehabil. 2015 Nov 4;12:97. doi: 10.1186/s12984-015-0086-5.

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

Author information

1
Department of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, 48109, MI, USA. jrkoller@umich.edu.
2
School of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, 48109, MI, USA. jacobsda@umich.edu.
3
School of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, 48109, MI, USA. ferrisdp@umich.edu.
4
Department of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, 48109, MI, USA. jacobsda@umich.edu.

Abstract

BACKGROUND:

Robotic 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.

METHODS:

We 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.

RESULTS:

Using 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.

CONCLUSIONS:

Our 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.

PMID:
26536868
PMCID:
PMC4634144
DOI:
10.1186/s12984-015-0086-5
[Indexed for MEDLINE]
Free PMC Article

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